Seal Phase4AE same-precision GPU numerical gate and audit limits

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# 当前研究状态 — Phase 4AD
# 当前研究状态 — Phase 4AE
已按用户授权建立独立分支 `codex/lif-author-semantics-lab`。
正式状态:**COMPLETE-ORACLE-VALIDATED-LAB-ONLY**。
学习仍未达到可用标准;不替换任何旧候选,不把实现检查PASS当作学习PASS。
独立新模型分支:`codex/lif-author-semantics-lab`。
正式状态:**PASS-ISOLATED-GPU-NUMERICAL-GATE**。
尚未完成新动力学全脑验证或conditioning,学习仍未达到可用标准。
- [关键结果、限制与下一步](docs/progress/phase4ad-interpretation.md)
- [完整实验报告](docs/progress/phase4ad-author-semantics.md)
- [实验预声明](models/male-cns-lif/author-semantics-lab-v1/PROTOCOL.md)
- [新产物封存核验](registry/phase4ad-author-semantics/seal-verification.json)
- [此前全脑输入背景复核](docs/progress/feedback-context-review-20260930.md)
- [本轮关键结果、限制和下一步](docs/progress/phase4ae-interpretation.md)
- [完整数值实验](docs/progress/phase4ae-author-runtime.md)
- [预先声明的协议](models/male-cns-lif/author-runtime-lab-v1/PROTOCOL.md)
- [交叉精度差异](registry/phase4ae-author-runtime/cross-precision-description.json)
- [校验与来源](registry/phase4ae-author-runtime/seal-verification.json)
- [上轮持续循环发现](docs/progress/phase4ad-interpretation.md)
## 关键发现
## 已完成
固定作者代码在Brian2 2.5.1实际运行。16项单元检查、10组真实回路/精度对照及其重复通过;58/58审计项通过。
恢复作者完整动力学后,五节点真实回路1秒放电从1,483降到610,但后500ms仍有306次。
因此不能认为“修g reset/不应期就解决学习问题”;持续循环仍存在,低活跃度不是晋级条件。
12组固定小图/压力case、两种精度、各重复3次,共72次GPU执行。
每次GPU与同精度实际Brian2参考的完整V/g history逐bit相同、最大误差0;事件、计数、不应期、请求送达及到达顺序/guard也一致。
旧五组全脑参考回归和原230,239项及后续封存产物hash再次通过。
下一步先做独立GPU实现的高入度/累加顺序与短时全图对照,再检验固定DoOR嗅觉输入是否保留区别;尚未开始conditioning。
输入请求、不应期拒绝和实际放电必须分开统计。后续学习必须执行真实无调制/冻结/随机奖励对照和等待期,而非复用标签。
float64与float32在临界阈值夹具分别放电4次/3次,因此不能把32位版本直接称为64位等价优化。
旧模型、base CSR、原synapse count、W_syn、transmitter signs、APL校准、eligibility-v2和14,232 mask均未修改。
## 冻结与服务
## 下一步
原230,239项以及4AA/4AB/4AC与背景复核封存文件前后校验不变。
旧deterministic-v2五组全脑参考重新运行,事件、计数、最终电压和g精确一致。
base CSR、raw counts、W_syn、transmitter signs、APL参数、eligibility-v2、14,232-edge mask未修改。
只在独立实验环境安装作者版本依赖;未停用或修改ComfyUI。
没有游戏、RL、decoder、视觉输入或权重训练,没有生成新的learned candidate。
先用float64做完整图的10/50ms固定数值对照,再进行固定DoOR嗅觉信息保持测试。
全部通过才进入真实学习验收:至少5个training seeds、独立evaluation、刺激特异性MBON/downstream,以及真正运行的对照和保持期。
未训练、未进入游戏/RL/decoder/视觉接口。ComfyUI保持原样。
代码、小报告、来源和校验清单进入Git;大数据及完整实验轨迹保留服务器,不等于完整异机备份。
Git仅同步源码、小报告和校验清单;大数据与完整实验轨迹在服务器,不等于完整异机备份。
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# Phase 4AE GPU作者语义数值门禁
状态:`PASS-ISOLATED-GPU-NUMERICAL-GATE`。
仅验证固定小网络,尚未证明全脑等价、生物学正确或学习可用。未训练、未修改冻结输入或旧模型。
| case | precision | events | max V/g error V | exact whole history | pass |
|---|---|---:|---:|---|---|
| fanin_bursts | float64 | 1539 | 0.0 | True | True |
| fanin_bursts | float32 | 1539 | 0.0 | True | True |
| highest_indegree | float64 | 101 | 0.0 | True | True |
| highest_indegree | float32 | 101 | 0.0 | True | True |
| mixed_high_indegree | float64 | 106 | 0.0 | True | True |
| mixed_high_indegree | float32 | 106 | 0.0 | True | True |
| motif_feedforward | float64 | 3 | 0.0 | True | True |
| motif_feedforward | float32 | 3 | 0.0 | True | True |
| motif_five | float64 | 60 | 0.0 | True | True |
| motif_five | float32 | 60 | 0.0 | True | True |
| motif_no_input | float64 | 0 | 0.0 | True | True |
| motif_no_input | float32 | 0 | 0.0 | True | True |
| motif_pair | float64 | 22 | 0.0 | True | True |
| motif_pair | float32 | 22 | 0.0 | True | True |
| motif_trio | float64 | 45 | 0.0 | True | True |
| motif_trio | float32 | 45 | 0.0 | True | True |
| ordered_cancellation | float64 | 32 | 0.0 | True | True |
| ordered_cancellation | float32 | 32 | 0.0 | True | True |
| release_selfloop | float64 | 8 | 0.0 | True | True |
| release_selfloop | float32 | 8 | 0.0 | True | True |
| signed_random | float64 | 400 | 0.0 | True | True |
| signed_random | float32 | 400 | 0.0 | True | True |
| threshold_rounding | float64 | 4 | 0.0 | True | True |
| threshold_rounding | float32 | 3 | 0.0 | True | True |
每项GPU执行3次,检查原始事件、输入请求实际送达、逐目标到达顺序及guard、计数、不应期和完整状态。
大整数、随机图、重复/零边属于纯数值夹具,不是新增MaleCNS连接或生理假设。真实子图使用全部既有诱导边、原contact count与冻结transmitter sign。
原始冻结完整性与旧五组全脑deterministic-v2回归再次核验;该旧回归PASS不等于新动力学全脑PASS。
没有根据活动强度选择参数;尚须短时全图对照及嗅觉信息保持测试。
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# Phase 4AE:独立GPU后端通过固定数值压力集
状态:`PASS-ISOLATED-GPU-NUMERICAL-GATE`。独立分支仍为 `codex/lif-author-semantics-lab`。
这不是 learned candidate,也不是全脑或长期动力学等价声明。
## 具体推进
- 新增双精度CUDA后端,按已验证作者表达式逐次舍入,禁用FMA和fast-math。
- 保留精确18-step delay与22-step refractory;只展开当时放电源的真实出边,不逐步扫描全部edge。
- 突触从目标当前g开始逐条累加,不能先求边权和再加;刚放电的目标拒绝同tick输入。
- 将实际请求、允许注入、不应期拒绝分开统计。没有采用作者poi接口的rfc=0策略。
- Brian2实际队列由只读代理观测;每个oracle再做无代理复跑,证明观测工具没有改变输出。
固定12组case × float64/float32 × 3次GPU执行,**72/72通过**。
全部状态history逐bit一致,V/g最大绝对差为0;事件、计数、不应期、请求送达、逐目标到达顺序和接受标记均一致。
三次GPU重复逐数组相同,24组Brian oracle的观测/无观测对照均通过。
这些是整个短轨迹的对照,不是仅比较总spike数或末态。
## 覆盖范围和未覆盖项
5个合成夹具覆盖有序正负抵消、256源fan-in、同tick输入与不应期释放、自环/队列回绕、阈值可表示数邻域,以及固定seed随机有符号网络。
极大整数、重复/零边只用于明确标记的数值夹具,不是真实MaleCNS新连接或生理参数。
2个真实128-neuron诱导子图按入度和原contact count选取,保持所有真实内部边与sign。
两者中心都是全图最高入度节点neuron_idx=5/body_id=10009/type=CT1,入度11,526;伙伴组成不同。
这是数值压力测试,不是两个独立中心的证据,也不是视觉编码实验。
另有原4AD五个固定真实motif的100ms前缀。还没有覆盖所有全图累加场景和长时间反馈。
## 精度差异不能隐藏
作者float64与作者float32在阈值临界夹具产生4次和3次事件,首次分歧在tick0。
两套GPU分别精确复制了各自参考,但**float32不能因此称为float64的等价加速**。
本轮没有放宽事件门槛,没有根据活动强度选模型。下一次全图首先使用作者默认float64,以减少精度混淆。
[完整交叉精度记录](../../registry/phase4ae-author-runtime/cross-precision-description.json)。
## 下一步与学习门槛
下一关:166,700节点/25,582,938边完整图的短时float64逐步对照:无输入10ms、固定单seed50ms、固定五seed50ms。
采用连续运行和流式状态记录,不保存dense spike矩阵;比较实际队列摘要及所有细胞的每tick状态。
通过后才进行固定DoOR嗅觉CAL,并独立审计原graded APL与新语义的组合。
只有信息保持、held-out区分和真实conditioning功能/对照通过,才能讨论学习候选;不能从本轮数值PASS跳到学习PASS。
## 完整性与运行信息
原230,239项及4AA–4AD、背景复核前后hash检查通过,旧五个deterministic-v2全脑参考再次精确一致。
新模型11个payload、新实验916个payload独立封存。ComfyUI未修改,科学环境未升级。
72次GPU诊断总wall约15.93s;诊断峰值allocated VRAM约5.45MB。
这些数值来自小图并包含诊断同步,**不是全脑性能或实时性承诺**;旧全脑回归峰值约286MB也不是新动力学性能。
大数据/完整状态只留服务器;Git同步源码、小报告、出处与checksum,不等于完整异机备份。
详见[完整实验报告](phase4ae-author-runtime.md)、[预声明](../../models/male-cns-lif/author-runtime-lab-v1/PROTOCOL.md)、[封存校验](../../registry/phase4ae-author-runtime/seal-verification.json)。
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# Phase 4AE — GPU author-semantics numerical gate
User authorized an independent model branch. This is not conditioning and does
not replace any Phase 1–4AD candidate/artifact. Learning remains unachieved.
The fixed author equations and all physical parameters come from the validated
4AD oracle, with dt=.1ms, delay=18 steps, refractory=22 steps and W=.275mV.
Canonical CSR, raw contact counts and frozen transmitter signs are read-only.
## Predeclared tests
Five synthetic numerical fixtures: ordered cancellation (including deliberately
nonphysiological large signed integers), 256-source fan-in bursts, refractory
release/self-loop collisions, representable threshold neighbours, and a fixed
seed signed random graph. Synthetic negative node labels are NOT MaleCNS IDs;
duplicate/zero edges occur only in labelled numerical fixtures.
Two 128-neuron real induced graphs are selected before simulation: the highest
in-degree target and its 127 largest-contact presynaptic partners; separately the
highest-in-degree target with >=64 positive and >=63 negative presynaptic inputs,
64 positive and 63 negative largest-contact partners. Ties break by canonical
neuron_idx. Include every existing edge within the selected node set, without
changing raw counts/signs or adding edges. This is numerical stress, not an input
interface or a physiological validation. Preserve identities and selection audit.
Also run the same five 4AD motifs for 100ms with one common seed request (or no
request for no_input). Report every outcome; do not select by activity/separability.
These tests do not exhaust all full-brain numerical corner cases.
## Exact arithmetic and schedule
Execute unchanged pinned author create_model in Brian2 2.5.1/NumPy1.22.3 using
the isolated 4AD environment. Export raw SI variables, actual coefficients and
weights. GPU float32 coefficients round to float32 before each multiply/add;
float64 uses double. No FMA or fast-math. Match generated expression order.
Update refractory and integrate; apply only currently permitted voltage seed
requests; strict threshold closes write access immediately; then accept delayed
anatomical arrivals in author order within each target, external synthetic input,
and reset. A fused reset is equivalent only if all arrivals reject current spikes.
Never replace sequential g+=w with g+sum(w). A queue proxy observes real Brian
arrival order without changing values; compare proxy and unwrapped runs exactly.
Use an exact spike ring buffer, no approximated delay or dense edge scan.
Before-threshold requests use the existing common requested-event convention,
not the author's distinct poi interface that changes target refractory to zero.
Report requested/accepted/rejected input separately. No graded APL in these isolated
tests; its future integration and frozen coefficients require explicit audit.
For each case/precision compare GPU to the same-precision real Brian oracle:
exact events, counts, refractory, input delivery and per-target arrival order/guard;
report full-history max/mean V/g error and bitwise state comparison. Fixed tolerance
is 1e-11V float64 / 1e-6V float32. Any event divergence fails, regardless of tolerance.
Each GPU case repeats three times with all arrays exactly equal. Cross-precision
differences are separately descriptive, not grounds to change thresholds.
## Integrity and progression
Preregister sources, old input hashes and deterministic fixture hashes before any
model execution. Preserve failures; use a separately recorded new attempt for
implementation repairs. Do not change frozen artifacts. Save manifests/SHA-256.
Old five deterministic-v2 full-brain reference regressions must pass again.
Only then consider a short full-graph author comparison, followed by DoOR CAL;
never claim full-brain equivalence or learning from isolated test PASS.
Author source/license/provenance are inherited read-only from 4AD. Supporting
primary documentation: https://numpy.org/doc/1.22/reference/generated/numpy.ufunc.at.html
and https://brian2.readthedocs.io/en/2.5.1/user/refractoriness.html . The fixed
generated source and actual boundary fixtures, not simplified prose, define timing.
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#include <torch/extension.h>
void author_state_cuda(torch::Tensor v, torch::Tensor g, torch::Tensor ref,
torch::Tensor requests, torch::Tensor spikes, torch::Tensor counts,
double gd, double c0, double cg, double cv, double threshold,
double reset, double seed_volt, int64_t refractory_steps);
void author_fill_edges_cuda(torch::Tensor indptr, torch::Tensor sources,
torch::Tensor offsets, torch::Tensor output);
void author_arrivals_cuda(torch::Tensor edges, torch::Tensor starts, torch::Tensor targets,
torch::Tensor weights, torch::Tensor g, torch::Tensor ref);
void author_external_cuda(torch::Tensor external, torch::Tensor g, torch::Tensor ref);
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("state_step", &author_state_cuda, "Author NumPy-ordered integration/threshold/reset");
m.def("fill_edges", &author_fill_edges_cuda, "Expand only active-source canonical CSR edges");
m.def("apply_arrivals", &author_arrivals_cuda, "Sequential per-target arrival additions");
m.def("apply_external", &author_external_cuda, "Post-anatomical external arrival additions");
}
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#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <c10/cuda/CUDAException.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <cmath>
#include <cstdint>
#include <limits>
namespace {
constexpr int BLOCK = 128;
void check_tensor(const torch::Tensor& x, const torch::Tensor& like,
at::ScalarType dtype, const char* name) {
TORCH_CHECK(x.is_cuda() && x.is_contiguous() && x.dim() == 1,
name, " must be a contiguous CUDA vector");
TORCH_CHECK(x.device() == like.device(), name, " is on a different CUDA device");
TORCH_CHECK(x.scalar_type() == dtype, name, " has the wrong dtype");
}
void check_state(const torch::Tensor& g, const torch::Tensor& ref) {
TORCH_CHECK(g.scalar_type() == torch::kFloat32 || g.scalar_type() == torch::kFloat64,
"state must be float32 or float64");
check_tensor(g, g, g.scalar_type(), "g");
check_tensor(ref, g, torch::kInt32, "ref");
TORCH_CHECK(g.numel() == ref.numel(), "g/ref length mismatch");
TORCH_CHECK(g.numel() <= std::numeric_limits<int>::max(), "too many neurons");
}
void index_range(const torch::Tensor& x, int64_t upper, const char* name) {
if (x.numel()) {
TORCH_CHECK(x.min().item<int64_t>() >= 0 && x.max().item<int64_t>() < upper,
name, " index out of bounds");
}
}
template <typename T> __device__ __forceinline__ T add_rn(T a, T b);
template <> __device__ __forceinline__ float add_rn(float a, float b) { return __fadd_rn(a, b); }
template <> __device__ __forceinline__ double add_rn(double a, double b) { return __dadd_rn(a, b); }
template <typename T> __device__ __forceinline__ T mul_rn(T a, T b);
template <> __device__ __forceinline__ float mul_rn(float a, float b) { return __fmul_rn(a, b); }
template <> __device__ __forceinline__ double mul_rn(double a, double b) { return __dmul_rn(a, b); }
template <typename T>
__global__ void state_kernel(T* v, T* g, int32_t* ref, const uint8_t* requests,
uint8_t* spikes, int32_t* counts, int n, T gd, T c0,
T cg, T cv, T threshold, T reset, T seed_volt, int ref_steps) {
const int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i >= n) return;
int r = ref[i];
r = r > 0 ? r - 1 : 0;
const bool available = r == 0;
T vi = v[i];
if (available) {
const T oldg = g[i];
const T oldv = vi;
// Match NumPy float-array expression evaluation: each operation rounds in T.
g[i] = mul_rn(gd, oldg);
const T driven = mul_rn(cg, oldg);
const T leaked = mul_rn(cv, oldv);
vi = add_rn(c0, add_rn(driven, leaked));
if (requests[i]) vi = seed_volt;
}
const bool fired = available && vi > threshold;
if (fired) {
vi = reset;
g[i] = T(0);
r = ref_steps;
counts[i] += 1;
}
// Nonavailable neurons retain V/g. Resetting fired cells before arrivals is
// equivalent to reset-after-arrivals because all their arrivals are rejected.
v[i] = vi;
ref[i] = r;
spikes[i] = fired ? 1 : 0;
}
__global__ void fill_kernel(const int64_t* indptr, const int64_t* sources,
const int64_t* offsets, int64_t* output, int n) {
const int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i >= n) return;
const int64_t source = sources[i];
const int64_t begin = indptr[source];
const int64_t end = indptr[source + 1];
for (int64_t j = begin; j < end; ++j) output[offsets[i] + j - begin] = j;
}
template <typename T>
__global__ void arrival_kernel(const int64_t* edges, const int64_t* starts,
const int32_t* targets, const T* weights, T* g,
const int32_t* ref, int segments, int64_t edge_count) {
const int segment = blockIdx.x * blockDim.x + threadIdx.x;
if (segment >= segments) return;
const int target = targets[segment];
if (ref[target] != 0) return;
const int64_t end = segment + 1 < segments ? starts[segment + 1] : edge_count;
// Start at existing g, never sum weights separately: cancellation can differ.
T accumulator = g[target];
for (int64_t j = starts[segment]; j < end; ++j)
accumulator = add_rn(accumulator, weights[edges[j]]);
g[target] = accumulator;
}
template <typename T>
__global__ void external_kernel(const T* external, T* g, const int32_t* ref, int n) {
const int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < n && ref[i] == 0 && external[i] != T(0))
g[i] = add_rn(g[i], external[i]);
}
} // namespace
void author_state_cuda(torch::Tensor v, torch::Tensor g, torch::Tensor ref,
torch::Tensor requests, torch::Tensor spikes, torch::Tensor counts,
double gd, double c0, double cg, double cv, double threshold,
double reset, double seed_volt, int64_t refractory_steps) {
check_state(g, ref);
const c10::cuda::CUDAGuard guard(g.device());
check_tensor(v, g, g.scalar_type(), "v");
check_tensor(requests, g, torch::kUInt8, "requests");
check_tensor(spikes, g, torch::kUInt8, "spikes");
check_tensor(counts, g, torch::kInt32, "counts");
const int64_t n = g.numel();
TORCH_CHECK(v.numel() == n && requests.numel() == n && spikes.numel() == n && counts.numel() == n,
"state vector length mismatch");
TORCH_CHECK(refractory_steps > 0 && refractory_steps <= std::numeric_limits<int>::max(),
"invalid refractory length");
for (double x : {gd, c0, cg, cv, threshold, reset, seed_volt})
TORCH_CHECK(std::isfinite(x), "nonfinite coefficient");
if (!n) return;
const auto stream = at::cuda::getCurrentCUDAStream(g.get_device());
AT_DISPATCH_FLOATING_TYPES(g.scalar_type(), "author_state", [&] {
state_kernel<scalar_t><<<(n + BLOCK - 1) / BLOCK, BLOCK, 0, stream>>>(
v.data_ptr<scalar_t>(), g.data_ptr<scalar_t>(), ref.data_ptr<int32_t>(),
requests.data_ptr<uint8_t>(), spikes.data_ptr<uint8_t>(), counts.data_ptr<int32_t>(),
static_cast<int>(n), static_cast<scalar_t>(gd), static_cast<scalar_t>(c0),
static_cast<scalar_t>(cg), static_cast<scalar_t>(cv), static_cast<scalar_t>(threshold),
static_cast<scalar_t>(reset), static_cast<scalar_t>(seed_volt), static_cast<int>(refractory_steps));
});
C10_CUDA_KERNEL_LAUNCH_CHECK();
}
void author_fill_edges_cuda(torch::Tensor indptr, torch::Tensor sources,
torch::Tensor offsets, torch::Tensor output) {
check_tensor(indptr, indptr, torch::kInt64, "indptr");
const c10::cuda::CUDAGuard guard(indptr.device());
check_tensor(sources, indptr, torch::kInt64, "sources");
check_tensor(offsets, indptr, torch::kInt64, "offsets");
check_tensor(output, indptr, torch::kInt64, "output");
TORCH_CHECK(indptr.numel() >= 1 && sources.numel() == offsets.numel(), "CSR shape mismatch");
TORCH_CHECK(sources.numel() <= std::numeric_limits<int>::max(), "too many sources");
TORCH_CHECK(indptr[0].item<int64_t>() == 0, "CSR indptr must begin at zero");
if (indptr.numel() > 1)
TORCH_CHECK((indptr.slice(0, 1) >= indptr.slice(0, 0, -1)).all().item<bool>(), "CSR indptr not monotonic");
index_range(sources, indptr.numel() - 1, "source");
if (!sources.numel()) {
TORCH_CHECK(output.numel() == 0, "nonempty expansion for no active sources");
return;
}
const auto degrees = indptr.index_select(0, sources + 1) - indptr.index_select(0, sources);
const auto expected_offsets = degrees.cumsum(0) - degrees;
TORCH_CHECK(torch::equal(offsets, expected_offsets), "incorrect output offsets");
TORCH_CHECK(degrees.sum().item<int64_t>() == output.numel(), "incorrect expansion length");
const auto stream = at::cuda::getCurrentCUDAStream(indptr.get_device());
fill_kernel<<<(sources.numel() + BLOCK - 1) / BLOCK, BLOCK, 0, stream>>>(
indptr.data_ptr<int64_t>(), sources.data_ptr<int64_t>(), offsets.data_ptr<int64_t>(),
output.data_ptr<int64_t>(), static_cast<int>(sources.numel()));
C10_CUDA_KERNEL_LAUNCH_CHECK();
}
void author_arrivals_cuda(torch::Tensor edges, torch::Tensor starts, torch::Tensor targets,
torch::Tensor weights, torch::Tensor g, torch::Tensor ref) {
check_state(g, ref);
const c10::cuda::CUDAGuard guard(g.device());
check_tensor(edges, g, torch::kInt64, "edges");
check_tensor(starts, g, torch::kInt64, "starts");
check_tensor(targets, g, torch::kInt32, "targets");
check_tensor(weights, g, g.scalar_type(), "weights");
TORCH_CHECK(starts.numel() == targets.numel(), "segment count mismatch");
TORCH_CHECK(starts.numel() <= std::numeric_limits<int>::max(), "too many target segments");
if (!starts.numel()) {
TORCH_CHECK(!edges.numel(), "edges without target segments");
return;
}
index_range(edges, weights.numel(), "edge");
index_range(starts, edges.numel(), "segment start");
index_range(targets, g.numel(), "target");
TORCH_CHECK(starts[0].item<int64_t>() == 0, "first segment must start at zero");
if (starts.numel() > 1) {
TORCH_CHECK((starts.slice(0, 1) > starts.slice(0, 0, -1)).all().item<bool>(), "segment starts not increasing");
TORCH_CHECK((targets.slice(0, 1) > targets.slice(0, 0, -1)).all().item<bool>(), "targets must be unique and increasing");
}
const auto stream = at::cuda::getCurrentCUDAStream(g.get_device());
AT_DISPATCH_FLOATING_TYPES(g.scalar_type(), "author_arrivals", [&] {
arrival_kernel<scalar_t><<<(starts.numel() + BLOCK - 1) / BLOCK, BLOCK, 0, stream>>>(
edges.data_ptr<int64_t>(), starts.data_ptr<int64_t>(), targets.data_ptr<int32_t>(),
weights.data_ptr<scalar_t>(), g.data_ptr<scalar_t>(), ref.data_ptr<int32_t>(),
static_cast<int>(starts.numel()), edges.numel());
});
C10_CUDA_KERNEL_LAUNCH_CHECK();
}
void author_external_cuda(torch::Tensor external, torch::Tensor g, torch::Tensor ref) {
check_state(g, ref);
const c10::cuda::CUDAGuard guard(g.device());
check_tensor(external, g, g.scalar_type(), "external");
TORCH_CHECK(external.numel() == g.numel(), "external/state length mismatch");
if (!g.numel()) return;
const auto stream = at::cuda::getCurrentCUDAStream(g.get_device());
AT_DISPATCH_FLOATING_TYPES(g.scalar_type(), "author_external", [&] {
external_kernel<scalar_t><<<(g.numel() + BLOCK - 1) / BLOCK, BLOCK, 0, stream>>>(
external.data_ptr<scalar_t>(), g.data_ptr<scalar_t>(), ref.data_ptr<int32_t>(), static_cast<int>(g.numel()));
});
C10_CUDA_KERNEL_LAUNCH_CHECK();
}
@@ -0,0 +1,12 @@
72601f46dde3202d62e71161958f620a3ce5b8f9c24cf1f5b7158289b5f9a3f0 PROTOCOL.md
208a937d17bbb041b9bf6b25b89264ee566830b60609e58491b44583d8541f15 author_ext.cpp
669f166e18ce9a3f63fc723db8b0a8b74f7fa4757194b1bd65b4cf6008a7787e author_ext.cu
356b6161576f6f6d79745af1927bb43b12a7c3d50922bf9e1bd29e473fcf2106 config.json
9eea9a592241cab7f4303850e969def7887eee3f4ec18421103605cafd258cf8 context.py
cb75d512ddf9a5ff344f3ac82eefb112406593bf7b414dfac79d7c5d730371ee finalize.py
03758d07572f4b44d272059d9308fac534268ad201b57bdd77d69fee95e67278 gpu_backend.py
32eb1c42383068ce52701d2139a376ce155ad7072cde019949f8ca2d55492409 oracle_stress.py
2519eaa2c50b2d91fd0c4f623aeabd6f5d212c5b537951d0a66bc81c569db78c prepare.py
92733a48921f95c3c0935bf16a704634b89b6540352f31cc1169ec6d3211b57a run_suite.py
720d04b379b6f1f64cae28a2203444a6d4c4e72ecb121cc69e93c2740fae69ab verify_frozen.py
7e8b0ac4adcd48eceabca7c9cc4f2c42a8746ebc673aee4490bae5a4746bbe12 manifest.json
@@ -0,0 +1,19 @@
{
"phase": "4AE",
"name": "author-runtime-lab-v1",
"human_authorization": "允许独立新模型分支测试动力学假设",
"seed": 410301,
"gpu_uuid": "GPU-79f3a85d-adce-7833-7003-6488140ca596",
"precisions": ["float64", "float32"],
"gpu_repeats": 3,
"max_abs_volt": {"float64": 1e-11, "float32": 1e-6},
"exact_events_required": true,
"exact_refractory_required": true,
"exact_arrival_order_per_tick_target_required": true,
"training": false,
"whole_brain_author_backend_claimed": false,
"candidate_generation": false,
"synthetic_cases": ["ordered_cancellation", "fanin_bursts", "release_selfloop", "threshold_rounding", "signed_random"],
"real_cases": ["highest_indegree", "mixed_high_indegree"],
"prior_motifs": ["pair", "trio", "five", "feedforward", "no_input"]
}
@@ -0,0 +1,40 @@
import hashlib
import json
from pathlib import Path
MODEL=Path(__file__).resolve().parent
ROOT=Path('/data/digital-drosophila')
EXP=ROOT/'experiments/phase4ae-author-runtime'
AD=ROOT/'experiments/phase4ad-author-semantics'
CFG=json.loads((MODEL/'config.json').read_text())
def digest(path):
h=hashlib.sha256()
with Path(path).open('rb') as f:
for b in iter(lambda:f.read(8<<20),b''):h.update(b)
return h.hexdigest()
def save(path,value):
path=Path(path);path.parent.mkdir(parents=True,exist_ok=True)
with path.open('x',encoding='utf-8') as f:json.dump(value,f,indent=2,ensure_ascii=False,allow_nan=False);f.write('\n')
def sources():
return {p.name:digest(p) for p in sorted(MODEL.iterdir()) if p.is_file() and p.name not in ('manifest.json','checksums.sha256')}
def check():
p=json.loads((EXP/'protocol/preregistered.json').read_text())
assert sources()==p['sources'],'Source changed after preregistration'
for row in p['inputs']:assert digest(ROOT/row['path'])==row['sha256'],row['path']
for row in json.loads((EXP/'protocol/case-hashes.json').read_text()):assert digest(EXP/row['path'])==row['sha256'],row['path']
return p
def integrity():
import sys
sys.path.insert(0,str(ROOT/'models/male-cns-lif/lif-semantics-audit-v1'))
import audit_semantics as old
r=old.integrity()
for rel in ('models/male-cns-lif/lif-semantics-audit-v1','experiments/phase4ac-lif-semantics',
'registry/feedback-context-review-20260930','models/male-cns-lif/author-semantics-lab-v1','experiments/phase4ad-author-semantics'):
r[rel]=old.manifest_check(ROOT/rel,digest)
assert all(x['pass'] for x in r.values())
return r
@@ -0,0 +1,52 @@
"""Gate the diagnostic prototype, never promote it as learned/full-brain runtime."""
import json
from pathlib import Path
import sys
from context import ROOT,EXP,MODEL,CFG,save,check,integrity,digest
from run_suite import load,compare
def seal(root):
rows=[]
for p in sorted(root.rglob('*')):
if '__pycache__' in p.parts or '.venv' in p.parts:continue
if p.is_file():rows.append(dict(path=str(p.relative_to(root)),bytes=p.stat().st_size,sha256=digest(p)))
assert not (root/'manifest.json').exists() and not (root/'checksums.sha256').exists()
save(root/'manifest.json',dict(root=str(root),files=rows))
with (root/'checksums.sha256').open('x') as f:
for r in rows:f.write(r['sha256']+' '+r['path']+'\n')
f.write(digest(root/'manifest.json')+' manifest.json\n')
def main():
check();after=integrity();save(EXP/'regression/old-integrity-after.json',after)
summary=json.loads((EXP/'regression/gpu-complete.json').read_text());ref=json.loads((EXP/'regression/frozen-five.json').read_text())
expected=(len(CFG['synthetic_cases'])+len(CFG['real_cases'])+len(CFG['prior_motifs']))*len(CFG['precisions'])*CFG['gpu_repeats']
assert len(summary['trials'])==expected
rows=[]
for record in summary['trials']:
name=record['case'];precision=record['precision'];repeat=record['repeat'];case=EXP/'cases'/name
oracle=load(EXP/'oracle'/(name+'-'+precision)/'state.npz');gpu=load(EXP/'gpu'/(name+'-'+precision+f'-r{repeat}')/'state.npz')
actual=compare(oracle,gpu,case,precision);assert actual==record['comparison'],'Stored report differs from raw states'
first=load(EXP/'gpu'/(name+'-'+precision+'-r0')/'state.npz')
import numpy as np
equal={k:bool(np.array_equal(v,gpu[k])) for k,v in first.items()};assert equal==record['repeat_arrays']
rows.append(record)
passed=all(r['pass_all'] for r in rows) and ref['pass_all'] and all(x['pass'] for x in after.values())
status='PASS-ISOLATED-GPU-NUMERICAL-GATE' if passed else 'FAIL-ISOLATED-GPU-NUMERICAL-GATE'
save(EXP/'audit/final-status.json',dict(status=status,pass_all=passed,cases=expected//6,gpu_runs=expected,
full_brain_author_equivalence=False,conditioning=False,learning_achieved=False,candidate_generated=False,
next_gate='Short full-graph same-precision author comparison, then fixed DoOR CAL only if numerical gates pass'))
lines=['# Phase 4AE GPU作者语义数值门禁','',f'状态:`{status}`。','',
'仅验证固定小网络,尚未证明全脑等价、生物学正确或学习可用。未训练、未修改冻结输入或旧模型。','',
'| case | precision | events | max V/g error V | exact whole history | pass |','|---|---|---:|---:|---|---|']
for r in rows:
if r['repeat']:continue
c=r['comparison'];lines.append(f"| {r['case']} | {r['precision']} | {c['oracle_events']} | {c['max_abs_volt']} | {c['bitwise_history']} | {r['pass_all']} |")
lines+=['','每项GPU执行3次,检查原始事件、输入请求实际送达、逐目标到达顺序及guard、计数、不应期和完整状态。',
'大整数、随机图、重复/零边属于纯数值夹具,不是新增MaleCNS连接或生理假设。真实子图使用全部既有诱导边、原contact count与冻结transmitter sign。',
'原始冻结完整性与旧五组全脑deterministic-v2回归再次核验;该旧回归PASS不等于新动力学全脑PASS。',
'没有根据活动强度选择参数;尚须短时全图对照及嗅觉信息保持测试。','']
with (EXP/'REPORT.md').open('x',encoding='utf-8') as f:f.write('\n'.join(lines))
check();seal(MODEL);seal(EXP);print(status,flush=True)
if not passed:raise SystemExit(1)
if __name__=='__main__':main()
@@ -0,0 +1,275 @@
"""Small-case diagnostic CUDA backend; SI states, explicit dtype rounding.
run_gpu is serial, records CPU diagnostics, and is not a performance runtime.
Coefficients and optional weights must come from the same-precision Brian2 oracle.
"""
from functools import lru_cache
import hashlib
import json
import math
import os
from pathlib import Path
import time
import traceback
import numpy as np
import torch
from torch.utils.cpp_extension import load
SOURCE_ROOT = Path(__file__).resolve().parent
COEFFICIENT_NAMES = ("gd", "c0", "cg", "cv", "threshold", "reset", "seed_volt")
def require(condition, message):
if not condition:
raise ValueError(message)
def write_json(path, data):
with Path(path).open("x", encoding="utf-8") as output:
json.dump(data, output, indent=2, allow_nan=False)
output.write("\n")
def digest(path):
checksum = hashlib.sha256()
with Path(path).open("rb") as source:
for block in iter(lambda: source.read(1 << 20), b""):
checksum.update(block)
return checksum.hexdigest()
@lru_cache(maxsize=None)
def _load_extension(build_path, signature):
build = Path(build_path) / ("author_cuda_" + signature[:16])
build.mkdir(parents=True, exist_ok=True)
module = load(name="author_cuda_" + signature[:16],
sources=[str(SOURCE_ROOT / "author_ext.cpp"), str(SOURCE_ROOT / "author_ext.cu")],
build_directory=str(build), extra_cflags=["-O2"],
extra_cuda_cflags=["-O2", "--fmad=false", "--ftz=false", "--prec-div=true", "--prec-sqrt=true"],
verbose=False)
return module
def build_extension(build_root):
provenance = {"sources": {name: digest(SOURCE_ROOT / name)
for name in ("author_ext.cpp", "author_ext.cu")},
"torch": torch.__version__, "torch_cuda": torch.version.cuda,
"arch_list": os.environ.get("TORCH_CUDA_ARCH_LIST"),
"cuda_flags": ["-O2", "--fmad=false", "--ftz=false", "--prec-div=true", "--prec-sqrt=true"]}
signature = hashlib.sha256(json.dumps(provenance, sort_keys=True).encode()).hexdigest()
return _load_extension(str(Path(build_root).resolve()), signature), provenance
def read_case(case_dir, precision, coefficients, weights):
case_dir = Path(case_dir)
require(precision in ("float32", "float64"), "precision must be float32 or float64")
dtype = np.dtype(precision)
with (case_dir / "spec.json").open(encoding="utf-8") as source:
spec = json.load(source)
for key in ("dt_ms", "steps", "duration_ms", "model"):
require(key in spec, "Missing spec field: " + key)
model = spec["model"]
for key in ("threshold_mv", "reset_potential_mv", "resting_potential_mv",
"synaptic_delay_ms", "refractory_ms", "w_syn_mv_per_anatomical_synapse"):
require(key in model and math.isfinite(float(model[key])), "Missing/nonfinite model field: " + key)
steps = spec["steps"]
require(type(steps) is int and 0 < steps < np.iinfo(np.int32).max, "Invalid step count")
dt = float(spec["dt_ms"])
require(math.isfinite(dt) and dt > 0, "Invalid dt")
require(math.isfinite(float(spec["duration_ms"])) and
round(float(spec["duration_ms"]) / dt) == steps, "Duration/steps mismatch")
delay = round(float(model["synaptic_delay_ms"]) / dt)
refractory = round(float(model["refractory_ms"]) / dt)
require(delay == 18 and refractory == 22, "This declared backend requires delay=18 and refractory=22")
require(float(model["w_syn_mv_per_anatomical_synapse"]) == 0.275, "W_syn differs from frozen value")
coeff = {}
for key in COEFFICIENT_NAMES:
require(key in coefficients and math.isfinite(float(coefficients[key])), "Missing/nonfinite coefficient: " + key)
coeff[key] = float(coefficients[key])
require(bool(np.isfinite(dtype.type(coeff[key]))), "Coefficient overflows requested dtype: " + key)
require(dtype.type(coeff["threshold"]) == dtype.type(float(model["threshold_mv"]) * .001),
"Oracle threshold differs from case spec")
require(dtype.type(coeff["reset"]) == dtype.type(float(model["reset_potential_mv"]) * .001),
"Oracle reset differs from case spec")
with np.load(case_dir / "arrays.npz", allow_pickle=False) as inputs:
arrays = {key: inputs[key].copy() for key in inputs.files}
required_types = {"nodes": "int32", "edge_id": "int64", "src": "int32", "dst": "int32",
"signed_raw": "int64", "requests": "uint8", "external_mv": "float64",
"initial_v_mv": "float64", "initial_g_mv": "float64"}
for key, expected in required_types.items():
require(key in arrays and arrays[key].dtype == np.dtype(expected), "Missing/wrong array dtype: " + key)
nodes = arrays["nodes"]
require(nodes.ndim == 1 and len(np.unique(nodes)) == len(nodes), "Invalid neuron identities")
n = len(nodes)
require(n < 512, "This diagnostic backend is restricted to fewer than 512 cells")
edge_id = arrays["edge_id"]
require(edge_id.ndim == 1 and len(np.unique(edge_id)) == len(edge_id), "Invalid edge identities")
e = len(edge_id)
for key in ("src", "dst", "signed_raw"):
require(arrays[key].shape == (e,), "Edge vector shape mismatch: " + key)
for key in ("src", "dst"):
require(bool(np.all((arrays[key] >= 0) & (arrays[key] < n))), "Local edge endpoint out of bounds")
require(np.array_equal(np.lexsort((edge_id, arrays["src"])), np.arange(e)),
"Edges must already be canonical source/edge_id sorted; indices will not be silently reordered")
for key in ("requests", "external_mv"):
require(arrays[key].shape == (steps, n), "Trial array shape mismatch: " + key)
require(bool(np.all(arrays["requests"] <= 1)), "Request array is not Boolean uint8")
for key in ("initial_v_mv", "initial_g_mv"):
require(arrays[key].shape == (n,), "Initial state shape mismatch: " + key)
for key in ("external_mv", "initial_v_mv", "initial_g_mv"):
require(bool(np.isfinite(arrays[key]).all()), "Nonfinite case array: " + key)
if weights is None:
weight_array = np.asarray(arrays["signed_raw"] * (.275 * .001), dtype=dtype)
weight_origin = "signed_raw * (0.275 * 0.001), then cast to requested dtype"
else:
weight_array = np.asarray(weights, dtype=dtype)
weight_origin = "caller supplied oracle SI weight array"
require(weight_array.shape == (e,) and bool(np.isfinite(weight_array).all()), "Invalid SI weights")
for key in ("initial_v_mv", "initial_g_mv", "external_mv"):
converted = np.asarray(arrays[key] * .001, dtype=dtype)
require(bool(np.isfinite(converted).all()), "Conversion overflow: " + key)
return spec, arrays, coeff, np.ascontiguousarray(weight_array), weight_origin
def run_gpu(case_dir, out_dir, precision, coefficients, build_root, weights=None):
"""Run one case; return state dict and save SI state plus GPU-selected arrivals.
``weights`` is optional SI volts in the original arrays.npz edge-row order.
Repeated calls share a cached extension; each needs a new exclusive out_dir.
"""
case_dir, out_dir = Path(case_dir), Path(out_dir)
require(not out_dir.exists(), "Refusing existing output directory: " + str(out_dir))
spec, arrays, coeff, weight_array, weight_origin = read_case(case_dir, precision, coefficients, weights)
require(torch.cuda.is_available(), "CUDA unavailable")
require(torch.cuda.device_count() == 1 and "4090" in torch.cuda.get_device_name(), "Only the single declared RTX4090 is permitted")
out_dir.mkdir(parents=True, exist_ok=False)
start = time.perf_counter()
try:
ext, build_provenance = build_extension(build_root)
build_done = time.perf_counter()
device = torch.device("cuda", torch.cuda.current_device())
torch.cuda.reset_peak_memory_stats(device)
dtype = torch.float32 if precision == "float32" else torch.float64
numpy_dtype = np.dtype(precision)
n, steps = len(arrays["nodes"]), spec["steps"]
def upload(value, target_dtype=None):
return torch.as_tensor(np.ascontiguousarray(value), device=device, dtype=target_dtype).contiguous()
v = upload(np.asarray(arrays["initial_v_mv"] * .001, dtype=numpy_dtype), dtype)
g = upload(np.asarray(arrays["initial_g_mv"] * .001, dtype=numpy_dtype), dtype)
ref = torch.zeros(n, dtype=torch.int32, device=device)
counts = torch.zeros(n, dtype=torch.int32, device=device)
requests = upload(arrays["requests"], torch.uint8)
accepted_requests = torch.empty_like(requests)
rejected_requests = torch.empty_like(requests)
external = upload(np.asarray(arrays["external_mv"] * .001, dtype=numpy_dtype), dtype)
synaptic_weights = upload(weight_array, dtype)
dst = upload(arrays["dst"], torch.int32)
degrees_np = np.bincount(arrays["src"], minlength=n).astype(np.int64)
indptr_np = np.concatenate((np.zeros(1, np.int64), np.cumsum(degrees_np, dtype=np.int64)))
indptr = upload(indptr_np, torch.int64)
ring = torch.zeros((19, n), dtype=torch.uint8, device=device)
history = torch.empty((steps, 3, n), dtype=dtype, device=device)
event_steps, event_nodes = [], []
arrival_steps, arrival_indices, arrival_accepted = [], [], []
torch.cuda.synchronize(device)
simulation_start = time.perf_counter()
for step in range(steps):
spikes = ring[step % 19]
# The state kernel decrements refractory before considering a request.
# These diagnostics use that exact pre-kernel eligibility on the GPU.
available_after_decrement = ref <= 1
accepted_requests[step].copy_(requests[step] & available_after_decrement.to(torch.uint8))
rejected_requests[step].copy_(requests[step] & (~available_after_decrement).to(torch.uint8))
ext.state_step(v, g, ref, requests[step], spikes, counts,
*[coeff[key] for key in COEFFICIENT_NAMES], 22)
if step >= 18:
due = ring[(step - 18) % 19]
sources = torch.nonzero(due, as_tuple=False).flatten().contiguous()
if sources.numel():
degrees = indptr[sources + 1] - indptr[sources]
offsets = (torch.cumsum(degrees, dim=0) - degrees).contiguous()
edge_count = int(degrees.sum().item())
expanded = torch.empty(edge_count, device=device, dtype=torch.int64)
ext.fill_edges(indptr, sources, offsets, expanded)
if edge_count:
destination = dst[expanded]
order = torch.argsort(destination, stable=True)
edge_indices = expanded[order].contiguous()
ordered_targets = destination[order]
boundary = torch.ones(edge_count, dtype=torch.bool, device=device)
boundary[1:] = ordered_targets[1:] != ordered_targets[:-1]
starts = torch.nonzero(boundary, as_tuple=False).flatten().contiguous()
targets = ordered_targets[starts].contiguous()
accepted = (ref[ordered_targets.long()] == 0).to(torch.uint8)
# Capture the actual CUDA-expanded and GPU-stable-sorted edge stream.
arrival_steps.append(np.full(edge_count, step, dtype=np.int32))
arrival_indices.append(edge_indices.cpu().numpy().copy())
arrival_accepted.append(accepted.cpu().numpy().copy())
ext.apply_arrivals(edge_indices, starts, targets, synaptic_weights, g, ref)
ext.apply_external(external[step], g, ref)
history[step, 0].copy_(v)
history[step, 1].copy_(g)
history[step, 2].copy_(ref)
fired = torch.nonzero(spikes, as_tuple=False).flatten().cpu().numpy()
if len(fired):
event_steps.append(np.full(len(fired), step, dtype=np.int32))
event_nodes.append(arrays["nodes"][fired])
torch.cuda.synchronize(device)
simulation_done = time.perf_counter()
def joined(chunks, dt):
return np.concatenate(chunks).astype(dt, copy=False) if chunks else np.empty(0, dtype=dt)
result = {"nodes": arrays["nodes"], "history": history.cpu().numpy(),
"counts": counts.cpu().numpy(), "v": v.cpu().numpy(), "g": g.cpu().numpy(),
"ref": ref.cpu().numpy(), "weights": synaptic_weights.cpu().numpy(),
"step": joined(event_steps, np.int32), "neuron_idx": joined(event_nodes, np.int32),
"request_attempted": arrays["requests"].copy(),
"request_accepted": accepted_requests.cpu().numpy(),
"request_rejected": rejected_requests.cpu().numpy()}
arrival_index = joined(arrival_indices, np.int64)
arrivals = {"step": joined(arrival_steps, np.int32), "edge_index": arrival_index,
"edge_id": arrays["edge_id"][arrival_index],
"target_local": arrays["dst"][arrival_index],
"accepted": joined(arrival_accepted, np.uint8)}
result.update(arrival_step=arrivals["step"], arrival_edge_index=arrivals["edge_index"],
arrival_accepted=arrivals["accepted"])
with (out_dir / "state.npz").open("xb") as target:
np.savez_compressed(target, **result)
with (out_dir / "arrival-order.npz").open("xb") as target:
np.savez_compressed(target, **arrivals)
finite = bool(all(np.isfinite(value).all() for value in result.values()))
metrics = {"precision": precision, "state_units": "SI volts; refractory is steps",
"coefficients_double_input": coeff,
"coefficients_effective_dtype": {key: float(numpy_dtype.type(value)) for key, value in coeff.items()},
"weights_origin": weight_origin, "nodes": n, "edges": len(weight_array), "steps": steps,
"total_spikes": len(result["step"]), "arriving_edges": len(arrival_index),
"accepted_arriving_edges": int(np.count_nonzero(arrivals["accepted"])),
"requested": int(np.count_nonzero(result["request_attempted"])),
"accepted_requests": int(np.count_nonzero(result["request_accepted"])),
"rejected_requests": int(np.count_nonzero(result["request_rejected"])),
"all_finite": finite, "delay_steps": 18, "refractory_steps": 22,
"build_or_cache_seconds": build_done - start,
"diagnostic_simulation_seconds": simulation_done - simulation_start,
"wall_seconds": time.perf_counter() - start,
"gpu": torch.cuda.get_device_name(device), "device": str(device),
"peak_allocated_vram_bytes": int(torch.cuda.max_memory_allocated(device)),
"peak_reserved_vram_bytes": int(torch.cuda.max_memory_reserved(device)),
"build": build_provenance,
"input_sha256": {name: digest(case_dir / name) for name in ("spec.json", "arrays.npz")},
"arrival_order_scope": "GPU expanded edges, stable target grouping; compare relative order per tick and target",
"performance_runtime_claim": False, "oracle_validation_claim": False,
"whole_brain_run": False, "learning": False,
"cpu_transfer_each_step_for_diagnostics": True}
write_json(out_dir / "metrics.json", metrics)
require(finite, "Nonfinite GPU state; raw trace preserved")
return result
except Exception as error:
write_json(out_dir / "error.json", {"type": type(error).__name__, "message": str(error),
"traceback": traceback.format_exc(), "precision": precision,
"validation_pass": False})
raise
@@ -0,0 +1,60 @@
{
"root": "/data/digital-drosophila/models/male-cns-lif/author-runtime-lab-v1",
"files": [
{
"path": "PROTOCOL.md",
"bytes": 4188,
"sha256": "72601f46dde3202d62e71161958f620a3ce5b8f9c24cf1f5b7158289b5f9a3f0"
},
{
"path": "author_ext.cpp",
"bytes": 1203,
"sha256": "208a937d17bbb041b9bf6b25b89264ee566830b60609e58491b44583d8541f15"
},
{
"path": "author_ext.cu",
"bytes": 10422,
"sha256": "669f166e18ce9a3f63fc723db8b0a8b74f7fa4757194b1bd65b4cf6008a7787e"
},
{
"path": "config.json",
"bytes": 805,
"sha256": "356b6161576f6f6d79745af1927bb43b12a7c3d50922bf9e1bd29e473fcf2106"
},
{
"path": "context.py",
"bytes": 1707,
"sha256": "9eea9a592241cab7f4303850e969def7887eee3f4ec18421103605cafd258cf8"
},
{
"path": "finalize.py",
"bytes": 3814,
"sha256": "cb75d512ddf9a5ff344f3ac82eefb112406593bf7b414dfac79d7c5d730371ee"
},
{
"path": "gpu_backend.py",
"bytes": 16277,
"sha256": "03758d07572f4b44d272059d9308fac534268ad201b57bdd77d69fee95e67278"
},
{
"path": "oracle_stress.py",
"bytes": 29235,
"sha256": "32eb1c42383068ce52701d2139a376ce155ad7072cde019949f8ca2d55492409"
},
{
"path": "prepare.py",
"bytes": 8828,
"sha256": "2519eaa2c50b2d91fd0c4f623aeabd6f5d212c5b537951d0a66bc81c569db78c"
},
{
"path": "run_suite.py",
"bytes": 5318,
"sha256": "92733a48921f95c3c0935bf16a704634b89b6540352f31cc1169ec6d3211b57a"
},
{
"path": "verify_frozen.py",
"bytes": 2157,
"sha256": "720d04b379b6f1f64cae28a2203444a6d4c4e72ecb121cc69e93c2740fae69ab"
}
]
}
@@ -0,0 +1,502 @@
"""Small, fixed-input Brian2 2.5.1 stress oracle for Phase 4AE.
The archived author source, equations, reset, parameters, and synapses are left
unchanged. Diagnostic initial states and the old common request interface are
explicit inputs. This file does not install packages or run whole-brain cases.
"""
from __future__ import annotations
import hashlib
import importlib.metadata
import json
import platform
import sys
import types
from pathlib import Path
import numpy as np
import pandas as pd
AUTHOR_PATH = Path("/data/digital-drosophila/experiments/phase4ad-author-semantics/external-resources/model.py")
AUTHOR_SHA256 = "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e"
AUTHOR_COMMIT = "91bdd1e7dcf193f3e7ca5a8933497fcef63b7960"
LIO_NAMES = {"gd": "_lio_2", "c0": "_lio_8", "cg": "_lio_9", "cv": "_lio_10"}
def _save(path: Path, value) -> None:
with path.open("x", encoding="utf-8") as handle:
json.dump(value, handle, indent=2, sort_keys=True, allow_nan=False)
handle.write("\n")
def _save_arrays(path: Path, arrays: dict) -> None:
with path.open("xb") as handle:
np.savez_compressed(handle, **arrays)
def _digest(path: Path) -> str:
h = hashlib.sha256()
with path.open("rb") as handle:
for block in iter(lambda: handle.read(8 << 20), b""):
h.update(block)
return h.hexdigest()
def _check(condition, message: str) -> None:
if not condition:
raise ValueError(message)
def _load_case(case_dir: Path) -> tuple[dict, dict]:
spec = json.loads((case_dir / "spec.json").read_text(encoding="utf-8"))
with np.load(case_dir / "arrays.npz", allow_pickle=False) as archive:
arrays = {name: archive[name].copy() for name in archive.files}
expected_types = {"nodes": "int32", "edge_id": "int64", "src": "int32",
"dst": "int32", "signed_raw": "int64", "requests": "uint8",
"external_mv": "float64", "initial_v_mv": "float64",
"initial_g_mv": "float64"}
for name, dtype in expected_types.items():
_check(name in arrays, f"Missing input array: {name}")
_check(arrays[name].dtype == np.dtype(dtype), f"{name} must have dtype {dtype}")
nodes = arrays["nodes"]
_check(nodes.ndim == 1 and 1 <= len(nodes) <= 512, "Stress cases require 1..512 nodes")
_check(np.all(np.diff(nodes.astype(np.int64)) > 0), "nodes must be sorted and unique")
steps = int(spec["steps"])
_check(steps == spec["steps"] and 1 <= steps <= 1000, "steps must be an integer in 1..1000")
_check(float(spec["dt_ms"]) == .1, "The fixed stress protocol requires dt_ms=0.1")
if "duration_ms" in spec:
_check(np.isclose(float(spec["duration_ms"]), steps * .1, rtol=0, atol=1e-12),
"duration_ms disagrees with steps and dt_ms")
if "dt_ms" in spec["model"]:
_check(float(spec["model"]["dt_ms"]) == .1, "model dt_ms disagrees with case dt_ms")
edge_count = len(arrays["edge_id"])
# The unchanged author calls Synapses.connect(i=[], j=[]), which Brian2
# 2.5.1 does not support. Empty-network fixtures must explicitly provide
# a documented zero-weight synthetic edge in the input case.
_check(edge_count > 0, "Use an explicit zero-weight fixture edge instead of empty connectivity")
for name in ("edge_id", "src", "dst", "signed_raw"):
_check(arrays[name].shape == (edge_count,), f"{name} has an inconsistent shape")
_check(len(np.unique(arrays["edge_id"])) == edge_count, "edge_id must be unique")
for name in ("src", "dst"):
_check(np.all((arrays[name] >= 0) & (arrays[name] < len(nodes))), f"{name} is out of local range")
_check(np.array_equal(np.lexsort((arrays["edge_id"], arrays["src"])), np.arange(edge_count)),
"Edges must be in canonical source-then-edge order")
for name in ("requests", "external_mv"):
_check(arrays[name].shape == (steps, len(nodes)), f"{name} must have shape (steps, N)")
_check(np.all(arrays["requests"] <= 1), "requests must contain only 0 or 1")
for name in ("initial_v_mv", "initial_g_mv"):
_check(arrays[name].shape == (len(nodes),), f"{name} must have shape (N,)")
for name in ("external_mv", "initial_v_mv", "initial_g_mv"):
_check(np.isfinite(arrays[name]).all(), f"{name} contains a nonfinite value")
return spec, arrays
def _code_records(network) -> list:
records = []
for obj in network.sorted_objects:
row = {"name": obj.name, "type": type(obj).__name__,
"when": obj.when, "order": int(obj.order), "active": bool(obj.active)}
if hasattr(obj, "abstract_code"):
abstract = obj.abstract_code
row["abstract_code"] = abstract if isinstance(abstract, (str, dict)) else str(abstract)
row["code_objects"] = [
{"name": code.name, "generated_code": str(code.code)}
for code in getattr(obj, "code_objects", ())]
records.append(row)
return records
class _QueueReadProxy:
"""Forward the queue unchanged; copy only observations from peek()."""
def __init__(self, queue, get_tick, get_available, targets, observations, counters):
self._original_queue = queue
self._get_tick = get_tick
self._get_available = get_available
self._targets = targets
self._observations = observations
self._counters = counters
def peek(self, *args, **kwargs):
value = self._original_queue.peek(*args, **kwargs)
edge_indices = np.asarray(value, dtype=np.int64).copy()
if np.any(edge_indices < 0) or np.any(edge_indices >= len(self._targets)):
raise AssertionError("The anatomical queue returned an invalid local edge index")
tick = self._get_tick()
accepted = np.asarray(self._get_available(), dtype=bool)[self._targets[edge_indices]].astype(np.uint8)
self._observations.append((tick, edge_indices, accepted))
self._counters["peek_calls"] += 1
return value
def advance(self, *args, **kwargs):
self._counters["advance_calls"] += 1
return self._original_queue.advance(*args, **kwargs)
def __getattr__(self, name):
return getattr(self._original_queue, name)
def _scalar_namespace(code_runner, name: str):
for code in getattr(code_runner, "code_objects", ()):
if name in code.namespace:
value = np.asarray(code.namespace[name])
if value.ndim == 0 and np.isfinite(value):
return float(value), {"source": "CodeObject.namespace", "code_object": code.name,
"name": name, "namespace_dtype": str(value.dtype)}
return None, None
def _coefficients(neu, parameters, b2, dt_seconds: float) -> dict:
generated = "\n".join(str(code.code) for code in neu.state_updater.code_objects)
_check("_lio_2 * g" in generated and
"_lio_8 + ((_lio_9 * g) + (_lio_10 * v))" in generated,
"Generated state expression changed; the recorded _lio-to-GPU mapping is not valid")
dt = np.float64(dt_seconds)
tau = np.float64(parameters["tau"] / b2.second)
membrane = np.float64(parameters["t_mbr"] / b2.second)
rest = np.float64(parameters["v_0"] / b2.volt)
# This is the actual scalar expression order emitted by the pinned Brian
# state updater, not an algebraically rearranged analytic solution.
lio_1 = (-dt) / tau
lio_3 = tau / (membrane - tau)
lio_4 = dt / membrane
lio_5 = dt / tau
lio_6 = (-dt) / membrane
fallback = {
"gd": np.exp(lio_1), "c0": rest - (rest * np.exp(lio_6)),
"cg": ((lio_3 * ((-np.exp(lio_4)) + np.exp(lio_5))) * np.exp(lio_6)) * np.exp(lio_1),
"cv": np.exp(lio_6),
}
result, provenance = {}, {}
for key, name in LIO_NAMES.items():
value, record = _scalar_namespace(neu.state_updater, name)
if value is None:
value = float(fallback[key])
record = {"source": "same generated scalar expression evaluated with numpy.float64", "name": name}
result[key], provenance[key] = value, record
for key, runner, name, author_key in (
("threshold", neu.thresholder["spike"], "v_th", "v_th"),
("reset", neu.resetter["spike"], "v_rst", "v_rst"),
):
value, record = _scalar_namespace(runner, name)
if value is None:
value = float(parameters[author_key] / b2.volt)
record = {"source": "unchanged author parameter", "name": name}
result[key], provenance[key] = value, record
result["seed_volt"] = float((parameters["v_th"] + 1 * b2.mV) / b2.volt)
provenance["seed_volt"] = {"source": "common request interface: unchanged v_th + 1 mV"}
result["metadata"] = {
"units": "SI volts and dimensionless coefficients", "names": LIO_NAMES,
"provenance": provenance, "hex": {key: value.hex() for key, value in result.items()},
"voltage_expression": "c0 + ((cg * old_g) + (cv * old_v))",
"g_expression": "gd * old_g",
"constant_values_are_double": True,
"float32_execution_note": "Brian NumPy scalar/array promotion and operation order must be matched separately; double coefficients alone do not assert float64 intermediate array arithmetic.",
}
return result
def _parameters_match(parameters, model, b2) -> dict:
rows = {}
for config_key, author_key, unit in (
("membrane_tau_ms", "t_mbr", b2.ms), ("synaptic_tau_ms", "tau", b2.ms),
("refractory_ms", "t_rfc", b2.ms), ("synaptic_delay_ms", "t_dly", b2.ms),
("resting_potential_mv", "v_0", b2.mV), ("reset_potential_mv", "v_rst", b2.mV),
("threshold_mv", "v_th", b2.mV), ("w_syn_mv_per_anatomical_synapse", "w_syn", b2.mV),
):
requested, actual = float(model[config_key]), float(parameters[author_key] / unit)
_check(np.isfinite(requested) and np.isclose(requested, actual, rtol=0, atol=1e-12),
f"Model parameter {config_key} differs from the archived author")
rows[config_key] = {"case": requested, "author": actual}
return rows
def _run_once(spec, arrays, precision, output, source_bytes, trace_queue):
import brian2 as b2
from brian2.core.base import BrianObject
output.mkdir(parents=True, exist_ok=False)
saved = {key: b2.prefs[key] for key in (
"codegen.target", "core.default_float_dtype", "legacy.refractory_timing")}
old_dt = b2.defaultclock.dt
dtype = np.dtype(precision)
steps, nodes = int(spec["steps"]), arrays["nodes"]
dt_ms, dt_seconds = float(spec["dt_ms"]), float(spec["dt_ms"]) * .001
try:
b2.start_scope()
b2.prefs["codegen.target"] = "numpy"
b2.prefs["core.default_float_dtype"] = dtype.type
b2.prefs["legacy.refractory_timing"] = False
b2.defaultclock.dt = dt_ms * b2.ms
author = types.ModuleType("phase4ae_author_" + precision)
author.__file__ = str(AUTHOR_PATH)
exec(compile(source_bytes, str(AUTHOR_PATH), "exec"), author.__dict__)
parameters = dict(author.default_params)
parameter_checks = _parameters_match(parameters, spec["model"], b2)
completeness = pd.DataFrame({"local_index": np.arange(len(nodes), dtype=np.int32)},
index=pd.Index(nodes, name="global_neuron_idx"))
connectivity = pd.DataFrame({
"Presynaptic_Index": arrays["src"], "Postsynaptic_Index": arrays["dst"],
"Excitatory x Connectivity": arrays["signed_raw"], "edge_id": arrays["edge_id"],
"source_neuron_idx": nodes[arrays["src"]], "target_neuron_idx": nodes[arrays["dst"]],
})
comp_path, con_path = output / "completeness.csv", output / "connectivity.parquet"
completeness.to_csv(comp_path)
connectivity.to_parquet(con_path, engine="pyarrow", index=False)
neu, syn, spikes = author.create_model(comp_path, con_path, parameters)
_check(np.dtype(neu.variables["v"].dtype) == dtype and np.dtype(neu.variables["g"].dtype) == dtype,
"Brian state dtype disagrees with the precision arm")
_check(np.dtype(syn.variables["w"].dtype) == dtype, "Brian synaptic weight dtype disagrees")
np.testing.assert_array_equal(np.asarray(syn.i[:]), arrays["src"])
np.testing.assert_array_equal(np.asarray(syn.j[:]), arrays["dst"])
neu.v = arrays["initial_v_mv"] * b2.mV
neu.g = arrays["initial_g_mv"] * b2.mV
initial_v = np.asarray(neu.v_[:], dtype=dtype).copy()
initial_g = np.asarray(neu.g_[:], dtype=dtype).copy()
attempted = arrays["requests"].copy()
accepted = np.zeros_like(attempted)
rejected = np.zeros_like(attempted)
request_slots_seen = np.zeros(steps, dtype=np.uint8)
def current_tick():
return int(round(float(neu.clock.t / b2.second) / dt_seconds))
def requests_before_thresholds():
tick = current_tick()
if tick < 0 or tick >= steps or request_slots_seen[tick]:
raise AssertionError("Common request operation executed outside its unique tick")
request_slots_seen[tick] = 1
request = attempted[tick].astype(bool)
available = np.asarray(neu.not_refractory[:], dtype=bool)
permitted = request & available
accepted[tick] = permitted
rejected[tick] = request & ~available
if permitted.any():
neu.v[np.flatnonzero(permitted)] = parameters["v_th"] + 1 * b2.mV
request_op = b2.NetworkOperation(requests_before_thresholds, clock=neu.clock,
when="before_thresholds", order=0, name="oracle_common_requests")
objects = [neu, syn, spikes, request_op]
ext_ticks, ext_targets = np.nonzero(arrays["external_mv"])
external_weights = np.empty(0, dtype=dtype)
if len(ext_ticks):
ext_ids = np.arange(len(ext_ticks), dtype=np.int32)
ext_source = b2.SpikeGeneratorGroup(len(ext_ticks), ext_ids,
ext_ticks.astype(np.float64) * dt_ms * b2.ms, sorted=True,
clock=neu.clock, name="zz_external_sources")
ext_syn = b2.Synapses(ext_source, neu, "w : volt", on_pre="g_post += w",
delay=0 * b2.ms, name="zz_external_arrivals")
ext_syn.connect(i=ext_ids, j=ext_targets)
ext_syn.w = arrays["external_mv"][ext_ticks, ext_targets] * b2.mV
ext_syn.pre.order = 0
_check(syn.pre.when == ext_syn.pre.when == "synapses" and syn.pre.order < 0,
"External delivery must follow the original anatomical pathway")
external_weights = np.asarray(ext_syn.w_[:], dtype=dtype).copy()
objects.extend([ext_source, ext_syn])
observations = []
queue_counters = {"peek_calls": 0, "advance_calls": 0, "installations": []}
if trace_queue:
class QueueInstaller(BrianObject):
def __init__(self):
super().__init__(clock=neu.clock, when="end", order=1_000_000,
name="zz_oracle_queue_installer")
def before_run(self, run_namespace):
# Network.before_run traverses sorted objects. All original
# code runners have compiled before this last end-slot object.
installed = []
for code in syn.pre.code_objects:
if "_queue" not in code.namespace or "_queue.peek()" not in str(code.code):
continue
original = code.namespace["_queue"]
if isinstance(original, _QueueReadProxy):
original = original._original_queue
code.namespace["_queue"] = _QueueReadProxy(
original, current_tick, lambda: neu.not_refractory[:],
arrays["dst"], observations, queue_counters)
installed.append(code.name)
if len(installed) != 1:
raise AssertionError(f"Expected exactly one anatomical queue consumer, got {installed}")
queue_counters["installations"].append(installed[0])
objects.append(QueueInstaller())
trace = b2.StateMonitor(neu, ["v", "g", "lastspike", "not_refractory"],
record=True, when="end", order=0, name="oracle_end_state")
objects.append(trace)
network = b2.Network(*objects)
_save(output / "schedule.json", {"schedule": list(network.schedule),
"summary": str(network.scheduling_summary())})
_save(output / "input-audit.json", {
"source_sha256": AUTHOR_SHA256, "parameter_checks": parameter_checks,
"source_parameters_modified": False, "precision": precision,
"dt_ms": dt_ms, "steps": steps, "request_interface": "before_thresholds; v=v_th+1mV only if not_refractory",
"poisson_helper_used": False, "all_neurons_keep_original_rfc": True,
"queue_trace_enabled": trace_queue, "state_units": "SI volts, original dtype",
"completeness_sha256": _digest(comp_path), "connectivity_sha256": _digest(con_path),
})
network.run(0 * b2.ms, namespace={})
_save(output / "compiled-code.json", _code_records(network))
if request_slots_seen.any() or observations:
raise AssertionError("Zero-duration compilation unexpectedly advanced the case")
network.run(steps * dt_ms * b2.ms, namespace={})
_save(output / "generated-code.json", _code_records(network))
_check(np.all(request_slots_seen == 1), "Not every request tick was observed exactly once")
event_float = np.asarray(spikes.t[:] / b2.second, dtype=np.float64) / dt_seconds
event_ticks = np.rint(event_float).astype(np.int32)
_check(np.allclose(event_float, event_ticks, rtol=0, atol=1e-6), "Spike time is off the integer clock")
local_indices = np.asarray(spikes.i[:], dtype=np.int32).copy()
order = np.lexsort((nodes[local_indices], event_ticks))
event_ticks, local_indices = event_ticks[order], local_indices[order]
fired = np.zeros_like(attempted)
fired[event_ticks, local_indices] = 1
counts = np.asarray(spikes.count[:], dtype=np.int32).copy()
_check(np.array_equal(fired.sum(axis=0), counts), "Events and per-neuron counts disagree")
_check(not np.any(accepted & ~fired), "An applicable common request did not produce a real spike")
_check(np.array_equal(attempted, accepted + rejected), "Request accounting is incomplete")
state_ticks = np.asarray(trace.t[:] / b2.second, dtype=np.float64) / dt_seconds
_check(state_ticks.shape == (steps,) and np.allclose(state_ticks, np.arange(steps), rtol=0, atol=1e-6),
"StateMonitor does not cover the exact requested ticks")
voltage = np.asarray(trace.v_, dtype=dtype).T.copy()
conductance = np.asarray(trace.g_, dtype=dtype).T.copy()
last = np.asarray(trace.lastspike_, dtype=np.float64).T
seen = np.isfinite(last) & (last >= 0)
last_ticks = np.zeros(last.shape, dtype=np.int64)
last_ticks[seen] = np.rint(last[seen] / dt_seconds).astype(np.int64)
refractory_steps = int(round(float(parameters["t_rfc"] / b2.ms) / dt_ms))
remaining = np.where(seen, np.maximum(refractory_steps - (np.arange(steps)[:, None] - last_ticks), 0), 0).astype(np.int32)
available_end = np.asarray(trace.not_refractory, dtype=bool).T
_check(np.array_equal(remaining == 0, available_end), "Refractory flag and integer countdown disagree")
history = np.stack([voltage, conductance, remaining.astype(dtype)], axis=1)
_check(np.isfinite(history).all(), "Author state became nonfinite")
arrival_step = np.empty(0, dtype=np.int32)
arrival_edge = np.empty(0, dtype=np.int64)
arrival_accepted = np.empty(0, dtype=np.uint8)
if trace_queue:
_check(queue_counters["peek_calls"] == steps and queue_counters["advance_calls"] == steps,
"Queue proxy did not observe exactly one peek and advance per tick")
_check([entry[0] for entry in observations] == list(range(steps)), "Queue peek ticks are not complete and ordered")
arrival_step = np.concatenate([np.full(len(ids), tick, dtype=np.int32) for tick, ids, _ in observations])
arrival_edge = np.concatenate([ids for _, ids, _ in observations]).astype(np.int64, copy=False)
arrival_accepted = np.concatenate([mask for _, _, mask in observations]).astype(np.uint8, copy=False)
delay_steps = int(round(float(parameters["t_dly"] / b2.ms) / dt_ms))
if len(arrival_step):
_check(np.all(arrival_step >= delay_steps), "An anatomical event arrived before the fixed delay")
_check(np.all(fired[arrival_step - delay_steps, arrays["src"][arrival_edge]]),
"A queued arrival has no matching presynaptic spike at the author delay")
_check(np.array_equal(arrival_accepted,
available_end[arrival_step, arrays["dst"][arrival_edge]].astype(np.uint8)),
"Observed arrival gating disagrees with the post-threshold refractory state")
source_counts_due = fired[:max(0, steps - delay_steps)].sum(axis=0, dtype=np.int64)
expected_arrivals = int(source_counts_due[arrays["src"]].sum(dtype=np.int64))
_check(len(arrival_edge) == expected_arrivals, "Queue trace omitted or duplicated an anatomical arrival")
result = {
"nodes": nodes.copy(), "step": event_ticks, "local_idx": local_indices,
"neuron_idx": nodes[local_indices], "counts": counts,
"v": voltage[-1].copy(), "g": conductance[-1].copy(), "ref": remaining[-1].copy(),
"history": history, "ref_history": remaining,
"not_refractory": available_end.astype(np.uint8),
"weights": np.asarray(syn.w_[:], dtype=dtype).copy(),
"initial_v": initial_v, "initial_g": initial_g,
"request_attempted": attempted, "request_accepted": accepted, "request_rejected": rejected,
"request_count": attempted.sum(axis=1, dtype=np.int32),
"applicable_count": accepted.sum(axis=1, dtype=np.int32),
"rejected_count": rejected.sum(axis=1, dtype=np.int32), "fired": fired,
"arrival_step": arrival_step, "arrival_edge_index": arrival_edge,
"arrival_accepted": arrival_accepted,
"external_step": ext_ticks.astype(np.int32), "external_target": ext_targets.astype(np.int32),
"external_weights": external_weights,
"external_accepted": available_end[ext_ticks, ext_targets].astype(np.uint8),
}
coefficients = _coefficients(neu, parameters, b2, dt_seconds)
_save(output / "coefficients.json", coefficients)
_save(output / "queue-audit.json", {
**queue_counters, "trace_enabled": trace_queue, "due_events": len(arrival_edge),
"accepted_due_events": int(arrival_accepted.sum()),
"all_due_events_preserved": True if trace_queue else None,
"ordering": "raw anatomical queue.peek local edge-index sequence; no sorting or filtering",
"proxy_contract": "peek returns original object; advance and all other methods forward unchanged",
})
_save_arrays(output / "state.npz", result)
return result, coefficients
except Exception as error:
_save(output / "error.json", {"type": type(error).__name__, "message": str(error)})
raise
finally:
for key, value in saved.items():
b2.prefs[key] = value
b2.defaultclock.dt = old_dt
def run_case(case_dir: Path, out_dir: Path, precision: str) -> dict:
"""Run one <=512-node, <=1000-step fixed case with exclusive outputs.
Main outputs are ``state.npz`` and ``coefficients.json``. Queue tracing is
enabled by default. ``spec.trace_queue`` can disable it; when enabled,
``spec.validate_queue_proxy`` defaults to True and repeats the same case
without instrumentation. That repeat must match every non-arrival array
exactly. An opt-out is recorded as unverified, never as a successful check.
This function changes global Brian preferences temporarily; run it serially.
"""
_check(precision in ("float64", "float32"), "precision must be float64 or float32")
case_dir, out_dir = Path(case_dir).resolve(strict=True), Path(out_dir).resolve()
spec, arrays = _load_case(case_dir)
source_bytes = AUTHOR_PATH.read_bytes()
source_hash = hashlib.sha256(source_bytes).hexdigest()
_check(source_hash == AUTHOR_SHA256, "Archived author source hash mismatch")
trace_queue = spec.get("trace_queue", True)
validate_proxy = spec.get("validate_queue_proxy", trace_queue)
_check(isinstance(trace_queue, bool) and isinstance(validate_proxy, bool), "Queue switches must be JSON booleans")
_check(not validate_proxy or trace_queue, "Proxy validation requires queue tracing")
out_dir.mkdir(parents=True, exist_ok=False)
try:
versions = {}
for name in ("brian2", "numpy", "pandas", "scipy", "pyarrow", "sympy", "cython"):
versions[name] = importlib.metadata.version(name)
_save(out_dir / "environment.json", {"python": sys.version, "platform": platform.platform(),
"packages": versions, "backend": "Brian2 numpy runtime"})
_check(versions["brian2"] == "2.5.1", "The stress oracle requires Brian2 2.5.1")
_check(versions["numpy"] == "1.22.3", "The stress oracle requires the recorded NumPy 1.22.3 promotion rules")
_save(out_dir / "case.json", {"spec": spec, "precision": precision,
"author_path": str(AUTHOR_PATH), "author_commit": AUTHOR_COMMIT, "author_sha256": source_hash,
"case_spec_sha256": _digest(case_dir / "spec.json"), "case_arrays_sha256": _digest(case_dir / "arrays.npz"),
"trace_queue": trace_queue, "validate_queue_proxy": validate_proxy,
"common_interface_preserved": True, "original_poi_interface_adopted": False,
"whole_brain": False, "learning": False})
result, coefficients = _run_once(spec, arrays, precision, out_dir / "instrumented",
source_bytes, trace_queue)
# Preserve the primary result even if the independent proxy check fails.
_save_arrays(out_dir / "state.npz", result)
_save(out_dir / "coefficients.json", coefficients)
validation = {"performed": False, "pass": None,
"reason": "Disabled by explicit case switch" if not validate_proxy else None}
if validate_proxy:
baseline, baseline_coefficients = _run_once(spec, arrays, precision, out_dir / "unwrapped-audit",
source_bytes, False)
checks = {key: bool(np.array_equal(value, baseline[key]))
for key, value in result.items() if not key.startswith("arrival_")}
coefficient_keys = ["gd", "c0", "cg", "cv", "threshold", "reset", "seed_volt"]
checks["coefficients"] = all(coefficients[key] == baseline_coefficients[key] for key in coefficient_keys)
validation = {"performed": True, "pass": bool(all(checks.values())),
"exact_arrays": checks,
"arrival_arrays_excluded": "An unwrapped queue is deliberately not observed"}
_save(out_dir / "proxy-validation.json", validation)
_check(not validate_proxy or validation["pass"], "Queue proxy changed an author result")
_check(_digest(AUTHOR_PATH) == source_hash, "Author source changed during the stress case")
summary = {"name": spec["name"], "precision": precision, "steps": int(spec["steps"]),
"nodes": len(arrays["nodes"]), "edges": len(arrays["edge_id"]),
"spikes": len(result["step"]), "requests": int(result["request_count"].sum()),
"applicable_requests": int(result["applicable_count"].sum()),
"rejected_requests": int(result["rejected_count"].sum()),
"due_arrivals": len(result["arrival_step"]),
"queue_trace_enabled": trace_queue, "queue_proxy_validated": validation["pass"],
"source_unchanged": True, "state_file": str(out_dir / "state.npz"),
"coefficient_file": str(out_dir / "coefficients.json"), "complete": True}
_save(out_dir / "summary.json", summary)
return {"arrays": result, "coefficients": coefficients, "summary": summary}
except Exception as error:
_save(out_dir / "error.json", {"type": type(error).__name__, "message": str(error)})
raise
@@ -0,0 +1,103 @@
"""Read-only selection and deterministic fixtures, before observing any dynamics."""
from datetime import datetime,timezone
import json
import subprocess
import numpy as np
import pandas as pd
import yaml
from context import MODEL,ROOT,EXP,AD,CFG,digest,save,sources,integrity
M=json.loads((AD/'protocol/preregistered.json').read_text())['config']['model']
def put(name,nodes,src,dst,raw,steps,description,edge_id=None,requests=None,external=None,v=None,g=None,metadata=None):
n=len(nodes);src=np.asarray(src,np.int32);dst=np.asarray(dst,np.int32);raw=np.asarray(raw,np.int64)
eid=np.arange(len(src),dtype=np.int64) if edge_id is None else np.asarray(edge_id,np.int64)
order=np.lexsort((eid,src));src,dst,raw,eid=src[order],dst[order],raw[order],eid[order]
assert len(src) and len(src)==len(dst)==len(raw) and np.all((src>=0)&(src<n)) and np.all((dst>=0)&(dst<n))
arrays=dict(nodes=np.asarray(nodes,np.int32),src=src,dst=dst,signed_raw=raw,edge_id=eid,
requests=np.zeros((steps,n),np.uint8) if requests is None else requests,
external_mv=np.zeros((steps,n),np.float64) if external is None else external,
initial_v_mv=np.full(n,M['resting_potential_mv'],np.float64) if v is None else v,
initial_g_mv=np.zeros(n,np.float64) if g is None else g)
folder=EXP/'cases'/name;folder.mkdir()
np.savez_compressed(folder/'arrays.npz',**arrays)
save(folder/'spec.json',dict(name=name,dt_ms=M['dt_ms'],steps=steps,duration_ms=steps*M['dt_ms'],model=M,
description=description,trace_queue=True,validate_queue_proxy=True,selection=metadata))
def synthetic():
n=33;t=160;raw=np.array([16777216,1,-16777216,1]+[(-1 if i%2 else 1)*(i+1) for i in range(28)],np.int64)
req=np.zeros((t,n),np.uint8);req[0,:32]=1;g=np.zeros(n);g[-1]=.123
put('ordered_cancellation',np.arange(-n,0),np.arange(32),np.full(32,32),raw,t,
'Pure synthetic numerical cancellation; huge counts are nonphysiological; nonzero initial g.',requests=req,g=g)
n=257;t=600;src=np.r_[np.arange(256),0,256];dst=np.full(258,256);raw=np.r_[np.where(np.arange(256)%2,-1,1)*(np.arange(256)%17+1)*13,19,11]
req=np.zeros((t,n),np.uint8)
for step in (0,21,22,40,62,101,150,300):req[step,:256]=1
req[0,256]=1;ext=np.zeros((t,n));ext[[0,21,22,23,300],256]=[1,1,1,-1,.5]
put('fanin_bursts',np.arange(-n,0),src,dst,raw,t,'256-source signed fan-in, labelled duplicate/loop; refractory requests and external writes.',requests=req,external=ext)
n=4;t=160;req=np.zeros((t,n),np.uint8);req[0,[0,1,2]]=1;req[22,2]=1;req[44,[0,1]]=1
ext=np.zeros((t,n));ext[[0,21,22,23,43,44,45],2]=[2,2,2,2,-1,1,1]
put('release_selfloop',np.arange(-n,0),[0,1,2,2,3],[2,2,2,3,2],[1,400,10,100,-1],t,'Release at22 with same-tick force/arrival, loops and ring wraps.',requests=req,external=ext)
dt=.0001;tm=.020;ts=.005;rest=-.052;th=-.045;cv=np.exp(-dt/tm);c0=rest-rest*cv
center=(th-c0)/cv;vals=[np.nextafter(center,-np.inf),center,np.nextafter(center,np.inf)]
center32=np.float32((np.float32(th)-np.float32(c0))/np.float32(cv));x=center32
for _ in range(3):x=np.nextafter(x,np.float32(-np.inf))
for _ in range(7):vals.append(float(x));x=np.nextafter(x,np.float32(np.inf))
n=len(vals);put('threshold_rounding',np.arange(-n,0),[0],[0],[0],100,
'Preintegration voltages bracket strict-threshold landing in both precisions; inert zero fixture edge.',v=np.asarray(vals)*1000)
rng=np.random.default_rng(CFG['seed']);n=128;t=400;src=np.repeat(np.arange(n),8);dst=rng.integers(0,n,len(src),dtype=np.int32)
raw=rng.integers(1,601,len(src),dtype=np.int64)*np.where(src%3==0,-1,1)
req=(rng.random((t,n))<.001).astype(np.uint8);req[200:]=0
g=rng.uniform(-1,1,n);ext=np.zeros((t,n));ext[::37]=rng.uniform(-.5,.5,(len(ext[::37]),n))
put('signed_random',np.arange(-n,0),src,dst,raw,t,'Fixed seed128-cell signed random fixture; forcing stops at20ms; signs assigned per synthetic source.',requests=req,external=ext,g=g)
def real():
can=ROOT/'datasets/male-cns-v1.0/processed/neuron-only';ptr=np.load(can/'gpu/csr-indptr.int64.npy',mmap_mode='r');dst=np.load(can/'gpu/csr-indices.int32.npy',mmap_mode='r');raw=np.load(can/'gpu/csr-weights.int64.npy',mmap_mode='r')
meta=pd.read_parquet(can/'neuron-index/unified-metadata.parquet').sort_values('neuron_idx').reset_index(drop=True)
assert np.array_equal(meta.neuron_idx,np.arange(166700)) and len(dst)==25582938 and int(raw.sum())==124177617
pol=yaml.safe_load((ROOT/'models/male-cns-lif/runtime-v1/phase4c.yaml').read_text())['sign_policy']
signs=[]
for nt in meta.neurotransmitter:
s='' if pd.isna(nt) else str(nt).strip().lower()
signs.append(pol['unknown_sign'] if not s else pol['unclear_sign'] if s=='unclear' else pol['neurotransmitter_sign'][s])
signs=np.asarray(signs,np.int8);src=np.repeat(np.arange(166700,dtype=np.int32),np.diff(ptr))
degree=np.bincount(dst,minlength=166700);pos=np.bincount(dst[signs[src]>0],minlength=166700);neg=np.bincount(dst[signs[src]<0],minlength=166700)
ranked=np.lexsort((np.arange(166700),-degree));targets=[int(ranked[0]),int(next(i for i in ranked if pos[i]>=64 and neg[i]>=63))]
for name,target in zip(CFG['real_cases'],targets):
incoming=np.flatnonzero((dst==target)&(src!=target));incoming=incoming[np.lexsort((src[incoming],-raw[incoming]))]
if name=='highest_indegree':chosen=incoming[:127]
else:
pp=incoming[signs[src[incoming]]>0][:64];nn=incoming[signs[src[incoming]]<0][:63]
assert len(pp)==64 and len(nn)==63,'Insufficient mixed partners for predeclared graph'
chosen=np.r_[pp,nn]
nodes=np.sort(np.unique(np.r_[src[chosen],target])).astype(np.int32);assert len(nodes)==128
lookup=np.full(166700,-1,np.int32);lookup[nodes]=np.arange(len(nodes),dtype=np.int32)
ids=np.concatenate([np.arange(ptr[s],ptr[s+1],dtype=np.int64) for s in nodes]);ids=ids[lookup[dst[ids]]>=0]
local_src=lookup[src[ids]];local_dst=lookup[dst[ids]];signed=raw[ids]*signs[src[ids]]
req=np.zeros((500,len(nodes)),np.uint8);stim=np.sort(src[chosen[:16]])
for tick in (0,25,50,100,150,200):req[tick,lookup[stim]]=1
put(name,nodes,local_src,local_dst,signed,500,'Deterministically selected real induced graph; canonical edge IDs/counts and frozen signs.',edge_id=ids,requests=req,
metadata=dict(target_neuron_idx=target,target_body_id=int(meta.body_id.iloc[target]),global_in_degree=int(degree[target]),positive_in_edges=int(pos[target]),negative_in_edges=int(neg[target]),stimulated_neuron_idx=stim.tolist()))
meta.iloc[nodes][['neuron_idx','body_id','annotation_type','neurotransmitter']].to_csv(EXP/'cases'/name/'identities.csv',index=False)
pd.DataFrame(dict(edge_id=ids,source_neuron_idx=src[ids],target_neuron_idx=dst[ids],raw_count=raw[ids],source_sign=signs[src[ids]],signed_raw=signed)).to_csv(EXP/'cases'/name/'anatomical-edges.csv',index=False)
ac=ROOT/'experiments/phase4ac-lif-semantics'
for name in CFG['prior_motifs']:
e=pd.read_csv(ac/'motifs'/f'{name}-edges.csv');nodes=np.load(ac/'motifs'/f'{name}-state.npz')['nodes'];look={int(v):i for i,v in enumerate(nodes)}
req=np.zeros((1000,len(nodes)),np.uint8)
if name!='no_input':req[0,look[1381]]=1
put('motif_'+name,nodes,[look[int(x)] for x in e.source_neuron_idx],[look[int(x)] for x in e.target_neuron_idx],e.raw_count.to_numpy()*signs[e.source_neuron_idx],1000,
'Same selected4AC/4AD motif,100ms prefix; no new edge or weight.',edge_id=e.edge_id.to_numpy(),requests=req)
def main():
assert not (EXP/'protocol').exists()
for d in ('protocol','cases','oracle','gpu','regression','audit','performance'):(EXP/d).mkdir()
can=ROOT/'datasets/male-cns-v1.0/processed/neuron-only'
inputs=[can/'gpu/csr-indptr.int64.npy',can/'gpu/csr-indices.int32.npy',can/'gpu/csr-weights.int64.npy',can/'neuron-index/unified-metadata.parquet',ROOT/'models/male-cns-lif/runtime-v1/phase4c.yaml',AD/'external-resources/model.py',AD/'manifest.json']
save(EXP/'protocol/preregistered.json',dict(timestamp_utc=datetime.now(timezone.utc).isoformat(),config=CFG,sources=sources(),
inputs=[dict(path=str(p.relative_to(ROOT)),sha256=digest(p)) for p in inputs],before_model_execution=True,
parent_commit=subprocess.check_output(['git','-C',str(ROOT),'rev-parse','HEAD'],text=True).strip()))
save(EXP/'regression/old-integrity-before.json',integrity())
synthetic();real()
save(EXP/'protocol/case-hashes.json',[dict(path=str(p.relative_to(EXP)),sha256=digest(p)) for p in sorted((EXP/'cases').rglob('*')) if p.is_file()])
print('PREPARED',len(list((EXP/'cases').iterdir())),'fixed cases; old integrity PASS',flush=True)
if __name__=='__main__':main()
@@ -0,0 +1,87 @@
"""Fixed Brian/GPU stress suite, with source-checked immutable inputs."""
import argparse
import json
import os
from pathlib import Path
import sys
import time
import numpy as np
from context import ROOT,MODEL,EXP,CFG,save,check
def load(path):
with np.load(path,allow_pickle=False) as f:return {k:f[k].copy() for k in f.files}
def compare(a,b,case,precision):
checks={k:bool(np.array_equal(a[k],b[k])) for k in ('step','neuron_idx','counts','ref')}
ah=np.asarray(a['history'],np.float64);bh=np.asarray(b['history'],np.float64)
assert ah.shape==bh.shape
delta=np.abs(ah[:,:2]-bh[:,:2]);limit=CFG['max_abs_volt'][precision]
checks['history_ref_exact']=bool(np.array_equal(ah[:,2],bh[:,2]))
checks['finite_states']=bool(np.isfinite(ah).all() and np.isfinite(bh).all())
checks['state_tolerance']=bool(delta.max()<=limit)
checks['weights_exact']=bool(np.array_equal(a['weights'],b['weights']))
# Distinguish logical event requests from realised requests after refractory.
for key in ('request_attempted','request_accepted','request_rejected'):
checks[key+'_exact']=bool(key in a and key in b and np.array_equal(a[key],b[key]))
data=load(case/'arrays.npz');targets=data['dst']
def arrival_sequence(result):
tick=result['arrival_step'];eid=result['arrival_edge_index'];guard=result['arrival_accepted']
assert tick.shape==eid.shape==guard.shape
# Different targets can interleave; order of writes to one target cannot.
order=np.lexsort((np.arange(len(eid)),targets[eid],tick))
return np.stack((tick[order],eid[order],guard[order]),axis=1)
aa=arrival_sequence(a);bb=arrival_sequence(b)
checks['arrival_order_and_guard_exact']=bool(np.array_equal(aa,bb))
first=None
for t in range(len(ah)):
if not np.array_equal(a['neuron_idx'][a['step']==t],b['neuron_idx'][b['step']==t]):first=t;break
bits_equal=a['history'].dtype==b['history'].dtype and np.array_equal(np.ascontiguousarray(a['history']).view(np.uint8),np.ascontiguousarray(b['history']).view(np.uint8))
return dict(pass_all=all(checks.values()),checks=checks,bitwise_history=bool(bits_equal),
max_abs_volt=float(delta.max()),mean_abs_volt=float(delta.mean()),fixed_limit_volt=limit,
first_event_divergence_step=first,oracle_events=int(len(a['step'])),gpu_events=int(len(b['step'])),
due_edges=int(len(aa)),accepted_edges=int(a['arrival_accepted'].sum()),
attempted_requests=int(a['request_attempted'].sum()),accepted_requests=int(a['request_accepted'].sum()))
def oracle():
from oracle_stress import run_case
t0=time.perf_counter();rows=[]
for case in sorted((EXP/'cases').iterdir()):
for precision in CFG['precisions']:
output=EXP/'oracle'/(case.name+'-'+precision)
run_case(case,output,precision)
r=load(output/'state.npz');assert np.isfinite(r['history']).all()
rows.append(dict(case=case.name,precision=precision,events=len(r['step'])))
print(json.dumps(rows[-1]),flush=True)
save(EXP/'regression/oracle-complete.json',dict(cases=rows,wall_seconds=time.perf_counter()-t0))
def gpu():
assert (EXP/'regression/oracle-complete.json').exists(),'No complete oracle suite'
os.environ['CUDA_VISIBLE_DEVICES']=CFG['gpu_uuid'];os.environ['CUDA_HOME']='/usr/local/cuda'
os.environ['PATH']=str(ROOT/'models/male-cns-lif/.venv/bin')+':/usr/local/cuda/bin:'+os.environ['PATH']
os.environ['MAX_JOBS']='2';os.environ['TORCH_CUDA_ARCH_LIST']='8.9'
sys.path.insert(0,str(ROOT/'models/male-cns-lif/terminal-scope-diagnostic-v1'))
from diagnostic import queues
queues()
from gpu_backend import run_gpu
t0=time.perf_counter();rows=[]
for case in sorted((EXP/'cases').iterdir()):
for precision in CFG['precisions']:
queues();refdir=EXP/'oracle'/(case.name+'-'+precision)
reference=load(refdir/'state.npz');coeff=json.loads((refdir/'coefficients.json').read_text());first=None
for repeat in range(CFG['gpu_repeats']):
out=EXP/'gpu'/(case.name+'-'+precision+f'-r{repeat}')
run_gpu(case,out,precision,coeff,EXP/'extension-build',weights=reference['weights'])
result=load(out/'state.npz');cmp=compare(reference,result,case,precision)
if first is None:first=result
rep={k:bool(np.array_equal(v,result[k])) for k,v in first.items()}
row=dict(case=case.name,precision=precision,repeat=repeat,comparison=cmp,repeat_arrays=rep,
pass_all=cmp['pass_all'] and all(rep.values()))
save(out/'comparison.json',row);rows.append(row)
print(json.dumps(dict(case=case.name,precision=precision,repeat=repeat,pass_all=row['pass_all'],
error_volt=cmp['max_abs_volt'],events=cmp['oracle_events'],bitwise_history=cmp['bitwise_history'])),flush=True)
save(EXP/'regression/gpu-complete.json',dict(pass_all=all(x['pass_all'] for x in rows),trials=rows,
wall_seconds=time.perf_counter()-t0,whole_brain_tested=False,learning_achieved=False))
if __name__=='__main__':
p=argparse.ArgumentParser();p.add_argument('mode',choices=['oracle','gpu']);args=p.parse_args()
check();globals()[args.mode]()
@@ -0,0 +1,33 @@
"""Run old frozen trials into new output only; no author-model equivalence claim."""
import os
import sys
import time
import numpy as np
import pandas as pd
import yaml
from context import ROOT,EXP,CFG,save,check
def main():
check();assert not (EXP/'regression/frozen-five.json').exists()
rtpath=ROOT/'models/male-cns-lif/deterministic-runtime-v2';pc=yaml.safe_load((rtpath/'phase4p.yaml').read_text())
os.environ['CUDA_VISIBLE_DEVICES']=CFG['gpu_uuid'];os.environ['CUDA_HOME']='/usr/local/cuda'
os.environ['PATH']=str(ROOT/'models/male-cns-lif/.venv/bin')+':/usr/local/cuda/bin:'+os.environ['PATH']
os.environ['MAX_JOBS']='2';os.environ['TORCH_CUDA_ARCH_LIST']='8.9'
sys.path.insert(0,str(ROOT/'models/male-cns-lif/terminal-scope-diagnostic-v1'))
from diagnostic import queues
queues();sys.path.insert(0,str(ROOT/'models/male-cns-lif'));sys.path.insert(0,str(rtpath))
import torch,phase4p
assert torch.cuda.mem_get_info()[0]>2*1024**3
torch.cuda.reset_peak_memory_stats();t0=time.perf_counter();rt=phase4p.CanonicalRuntime(pc,rtpath,EXP);rows=[]
for spec in pc['reference_trials']:
queues();base=ROOT/'experiments/phase4p-deterministic-runtime/phase4c-v2/reference'/spec['name']
ev=pd.read_parquet(base/'spike-events.parquet')
ref=dict(step=ev.step.to_numpy(),neuron_idx=ev.neuron_idx.to_numpy(),counts=np.load(base/'per-neuron-spike-counts.int32.npy'),v=np.load(base/'final-voltage.float32.npy'),g=np.load(base/'final-g.float32.npy'))
r=rt.run(np.flatnonzero(rt.pops[spec['population']]),spec['rate_hz'],spec['seed'],spec['duration_ms'])
row=dict(trial=spec['name'],exact={k:bool(np.array_equal(v,r[k])) for k,v in ref.items()},wall_seconds=r['metrics']['wall_seconds'])
rows.append(row);print(row,flush=True)
save(EXP/'regression/frozen-five.json',dict(pass_all=all(all(r['exact'].values()) for r in rows),trials=rows,
wall_seconds=time.perf_counter()-t0,peak_allocated_vram=int(torch.cuda.max_memory_allocated()),oracle='old frozen deterministic-v2 only'))
assert all(all(r['exact'].values()) for r in rows)
if __name__=='__main__':main()
@@ -0,0 +1,149 @@
{
"cases": [
{
"case": "fanin_bursts",
"event_equal": true,
"events_float64": 1539,
"events_float32": 1539,
"first_event_divergence_step": null,
"max_vg_abs_volt": 1.538265524056448e-07,
"input_hashes": {
"experiments/phase4ae-author-runtime/oracle/fanin_bursts-float64/state.npz": "48d4cf57a0fd4cd83dfad8497042063022a3a6b2214106080da69deaaaf5fa9e",
"experiments/phase4ae-author-runtime/oracle/fanin_bursts-float32/state.npz": "660c10c905097d76d91bea09c67ca861534196fc720d0e12102bac074a8525f5"
}
},
{
"case": "highest_indegree",
"event_equal": true,
"events_float64": 101,
"events_float32": 101,
"first_event_divergence_step": null,
"max_vg_abs_volt": 2.132226918638569e-07,
"input_hashes": {
"experiments/phase4ae-author-runtime/oracle/highest_indegree-float64/state.npz": "768d39cdeaa17a8b0fa7a64be5ffbe771e4d00c8db2709c4c5cdb0749f6c3455",
"experiments/phase4ae-author-runtime/oracle/highest_indegree-float32/state.npz": "11de500da330704334d9ce2d128aa2bba32decef851c75bc720a59f91fe534d2"
}
},
{
"case": "mixed_high_indegree",
"event_equal": true,
"events_float64": 106,
"events_float32": 106,
"first_event_divergence_step": null,
"max_vg_abs_volt": 2.1416214047303983e-07,
"input_hashes": {
"experiments/phase4ae-author-runtime/oracle/mixed_high_indegree-float64/state.npz": "c267e765682525fce86a72d5a86eb921c2d1cef4200172cdc48d1fb41634d727",
"experiments/phase4ae-author-runtime/oracle/mixed_high_indegree-float32/state.npz": "5df4b542b303e747bbbfbdb057da15e522465e54d6617349ee99f7192e1603f9"
}
},
{
"case": "motif_feedforward",
"event_equal": true,
"events_float64": 3,
"events_float32": 3,
"first_event_divergence_step": null,
"max_vg_abs_volt": 2.0734813362816018e-07,
"input_hashes": {
"experiments/phase4ae-author-runtime/oracle/motif_feedforward-float64/state.npz": "21f5487f461f02c60c8b23be2f7def85c52d54f95fd00dbcd9b5f1d16d933c3a",
"experiments/phase4ae-author-runtime/oracle/motif_feedforward-float32/state.npz": "e24ef4caddd186832c767df704c0073253735a6bbfa4381d76a23614ef7c0f8f"
}
},
{
"case": "motif_five",
"event_equal": true,
"events_float64": 60,
"events_float32": 60,
"first_event_divergence_step": null,
"max_vg_abs_volt": 1.8507321130484256e-07,
"input_hashes": {
"experiments/phase4ae-author-runtime/oracle/motif_five-float64/state.npz": "cf6ac75da0a75ccdd5ab6ac1b282fae8b8a07128f3e0a3f0106739348f75dfbb",
"experiments/phase4ae-author-runtime/oracle/motif_five-float32/state.npz": "3463e35fa10471d5abfd8090db1967fd5c2046364ef4e6b7974b710eb225fff2"
}
},
{
"case": "motif_no_input",
"event_equal": true,
"events_float64": 0,
"events_float32": 0,
"first_event_divergence_step": null,
"max_vg_abs_volt": 1.0728836014051168e-09,
"input_hashes": {
"experiments/phase4ae-author-runtime/oracle/motif_no_input-float64/state.npz": "4f8b98d81b6b91aeccdf59788e7cfa6a770fe1ac59753fa8c4777776e559fbe1",
"experiments/phase4ae-author-runtime/oracle/motif_no_input-float32/state.npz": "fbf19ad2fa292ecfcfbf893047ed0ee1dc411fd7825f728b21147349085177ad"
}
},
{
"case": "motif_pair",
"event_equal": true,
"events_float64": 22,
"events_float32": 22,
"first_event_divergence_step": null,
"max_vg_abs_volt": 3.596376418196767e-08,
"input_hashes": {
"experiments/phase4ae-author-runtime/oracle/motif_pair-float64/state.npz": "236fcb984139a2e376b026fe33e5fb079a1c3a9a4f1090925fbe2643c92fa31b",
"experiments/phase4ae-author-runtime/oracle/motif_pair-float32/state.npz": "016d9e6cd2e38e3ac4bf5dcd4526771f30885a7152b77e703bfa6debbed047f1"
}
},
{
"case": "motif_trio",
"event_equal": true,
"events_float64": 45,
"events_float32": 45,
"first_event_divergence_step": null,
"max_vg_abs_volt": 3.596376418196767e-08,
"input_hashes": {
"experiments/phase4ae-author-runtime/oracle/motif_trio-float64/state.npz": "bf670c8bc344163408fb96baf2df5d0fab9804f134fdd9011205ff6873d2084a",
"experiments/phase4ae-author-runtime/oracle/motif_trio-float32/state.npz": "ce04a18d25df422285be6ee80d36c949da506ebc885a87d31e109ee3d31f292b"
}
},
{
"case": "ordered_cancellation",
"event_equal": true,
"events_float64": 32,
"events_float32": 32,
"first_event_divergence_step": null,
"max_vg_abs_volt": 0.00012916631594562123,
"input_hashes": {
"experiments/phase4ae-author-runtime/oracle/ordered_cancellation-float64/state.npz": "1ad4991463a440d466ec28bc32bb35572f84b351716a8150ed6360d90d36942b",
"experiments/phase4ae-author-runtime/oracle/ordered_cancellation-float32/state.npz": "c937b1ec1e1ec63d1f0aedd3ab91b506f745849f3b66d8223d0d831bd9a689b0"
}
},
{
"case": "release_selfloop",
"event_equal": true,
"events_float64": 8,
"events_float32": 8,
"first_event_divergence_step": null,
"max_vg_abs_volt": 3.4269942050801916e-08,
"input_hashes": {
"experiments/phase4ae-author-runtime/oracle/release_selfloop-float64/state.npz": "2615645c5ae1bea1d9ba279c6639e35209907e4e103db6d10c05ad23dc11919d",
"experiments/phase4ae-author-runtime/oracle/release_selfloop-float32/state.npz": "dd8c3ac01e4d8035d94d0772efbb81948ba427f5ef66caf8d104f4819845a3cc"
}
},
{
"case": "signed_random",
"event_equal": true,
"events_float64": 400,
"events_float32": 400,
"first_event_divergence_step": null,
"max_vg_abs_volt": 1.4282541487831146e-06,
"input_hashes": {
"experiments/phase4ae-author-runtime/oracle/signed_random-float64/state.npz": "148f6098bb58c952afe07b680a79355091cfb7d6c21e2fc49fc4c902a191cafd",
"experiments/phase4ae-author-runtime/oracle/signed_random-float32/state.npz": "8657a63c2ea9703b8e37b0339eb63c24f31c597cf20cf214bb5e088312489d4a"
}
},
{
"case": "threshold_rounding",
"event_equal": false,
"events_float64": 4,
"events_float32": 3,
"first_event_divergence_step": 0,
"max_vg_abs_volt": 0.006999998211860661,
"input_hashes": {
"experiments/phase4ae-author-runtime/oracle/threshold_rounding-float64/state.npz": "2b7071d125814ce8855806157f8ff5b778f554076e6ab8f2488ad99fd8b447e0",
"experiments/phase4ae-author-runtime/oracle/threshold_rounding-float32/state.npz": "e225d3b95c5455cfb5f850e8fc3b3c97e99b4bd87bbfce6c2e6f764f32432a44"
}
}
],
"meaning": "Descriptive only; no changed tolerances, no variant selected by activity. Each GPU arm is validated against its own precision Brian oracle."
}
File diff suppressed because it is too large Load Diff
@@ -0,0 +1,37 @@
# Phase 4AE GPU作者语义数值门禁
状态:`PASS-ISOLATED-GPU-NUMERICAL-GATE`。
仅验证固定小网络,尚未证明全脑等价、生物学正确或学习可用。未训练、未修改冻结输入或旧模型。
| case | precision | events | max V/g error V | exact whole history | pass |
|---|---|---:|---:|---|---|
| fanin_bursts | float64 | 1539 | 0.0 | True | True |
| fanin_bursts | float32 | 1539 | 0.0 | True | True |
| highest_indegree | float64 | 101 | 0.0 | True | True |
| highest_indegree | float32 | 101 | 0.0 | True | True |
| mixed_high_indegree | float64 | 106 | 0.0 | True | True |
| mixed_high_indegree | float32 | 106 | 0.0 | True | True |
| motif_feedforward | float64 | 3 | 0.0 | True | True |
| motif_feedforward | float32 | 3 | 0.0 | True | True |
| motif_five | float64 | 60 | 0.0 | True | True |
| motif_five | float32 | 60 | 0.0 | True | True |
| motif_no_input | float64 | 0 | 0.0 | True | True |
| motif_no_input | float32 | 0 | 0.0 | True | True |
| motif_pair | float64 | 22 | 0.0 | True | True |
| motif_pair | float32 | 22 | 0.0 | True | True |
| motif_trio | float64 | 45 | 0.0 | True | True |
| motif_trio | float32 | 45 | 0.0 | True | True |
| ordered_cancellation | float64 | 32 | 0.0 | True | True |
| ordered_cancellation | float32 | 32 | 0.0 | True | True |
| release_selfloop | float64 | 8 | 0.0 | True | True |
| release_selfloop | float32 | 8 | 0.0 | True | True |
| signed_random | float64 | 400 | 0.0 | True | True |
| signed_random | float32 | 400 | 0.0 | True | True |
| threshold_rounding | float64 | 4 | 0.0 | True | True |
| threshold_rounding | float32 | 3 | 0.0 | True | True |
每项GPU执行3次,检查原始事件、输入请求实际送达、逐目标到达顺序及guard、计数、不应期和完整状态。
大整数、随机图、重复/零边属于纯数值夹具,不是新增MaleCNS连接或生理假设。真实子图使用全部既有诱导边、原contact count与冻结transmitter sign。
原始冻结完整性与旧五组全脑deterministic-v2回归再次核验;该旧回归PASS不等于新动力学全脑PASS。
没有根据活动强度选择参数;尚须短时全图对照及嗅觉信息保持测试。
@@ -0,0 +1,11 @@
{
"status": "PASS-ISOLATED-GPU-NUMERICAL-GATE",
"pass_all": true,
"cases": 12,
"gpu_runs": 72,
"full_brain_author_equivalence": false,
"conditioning": false,
"learning_achieved": false,
"candidate_generated": false,
"next_gate": "Short full-graph same-precision author comparison, then fixed DoOR CAL only if numerical gates pass"
}
@@ -0,0 +1,21 @@
{
"name": "fanin_bursts",
"dt_ms": 0.1,
"steps": 600,
"duration_ms": 60.0,
"model": {
"dt_ms": 0.1,
"membrane_tau_ms": 20.0,
"synaptic_tau_ms": 5.0,
"refractory_ms": 2.2,
"resting_potential_mv": -52.0,
"reset_potential_mv": -52.0,
"threshold_mv": -45.0,
"synaptic_delay_ms": 1.8,
"w_syn_mv_per_anatomical_synapse": 0.275
},
"description": "256-source signed fan-in, labelled duplicate/loop; refractory requests and external writes.",
"trace_queue": true,
"validate_queue_proxy": true,
"selection": null
}
@@ -0,0 +1,321 @@
edge_id,source_neuron_idx,target_neuron_idx,raw_count,source_sign,signed_raw
4897,5,21,2,-1,-2
4914,5,775,12,-1,-12
4919,5,1572,3,-1,-3
6148,5,52515,15,-1,-15
6240,5,57252,9,-1,-9
6305,5,59937,10,-1,-10
6310,5,60155,17,-1,-17
6320,5,60450,9,-1,-9
6329,5,60737,11,-1,-11
6351,5,61164,12,-1,-12
6354,5,61387,9,-1,-9
6368,5,61721,13,-1,-13
6371,5,61804,11,-1,-11
6378,5,61941,9,-1,-9
6396,5,62257,12,-1,-12
6412,5,62700,12,-1,-12
6415,5,62734,8,-1,-8
6421,5,62795,9,-1,-9
6428,5,62888,12,-1,-12
6435,5,63041,12,-1,-12
6437,5,63106,9,-1,-9
6440,5,63173,14,-1,-14
6448,5,63287,7,-1,-7
6452,5,63339,10,-1,-10
6491,5,63918,13,-1,-13
6505,5,64210,13,-1,-13
6508,5,64233,9,-1,-9
6521,5,64443,11,-1,-11
6529,5,64514,14,-1,-14
6550,5,65001,14,-1,-14
6568,5,65235,5,-1,-5
6569,5,65262,12,-1,-12
6574,5,65374,13,-1,-13
6584,5,65565,17,-1,-17
6598,5,65691,10,-1,-10
6602,5,65742,7,-1,-7
6617,5,66003,12,-1,-12
6626,5,66127,15,-1,-15
6636,5,66225,10,-1,-10
6637,5,66237,9,-1,-9
6660,5,66541,8,-1,-8
6666,5,66598,14,-1,-14
6693,5,66843,11,-1,-11
6705,5,66958,15,-1,-15
6714,5,67072,14,-1,-14
6722,5,67135,13,-1,-13
6738,5,67241,15,-1,-15
6743,5,67284,13,-1,-13
6749,5,67324,6,-1,-6
6754,5,67373,16,-1,-16
6765,5,67453,15,-1,-15
6770,5,67483,16,-1,-16
6776,5,67537,12,-1,-12
6783,5,67594,11,-1,-11
6832,5,68056,10,-1,-10
6836,5,68074,14,-1,-14
6854,5,68192,11,-1,-11
6856,5,68199,11,-1,-11
6872,5,68355,9,-1,-9
6894,5,68543,16,-1,-16
6904,5,68617,10,-1,-10
6910,5,68656,16,-1,-16
6934,5,68838,9,-1,-9
6937,5,68856,11,-1,-11
6938,5,68869,7,-1,-7
6970,5,69139,16,-1,-16
7007,5,69415,5,-1,-5
7019,5,69516,6,-1,-6
7022,5,69540,11,-1,-11
7064,5,69809,14,-1,-14
7073,5,69860,10,-1,-10
7084,5,69966,9,-1,-9
7087,5,69982,7,-1,-7
7092,5,70036,13,-1,-13
7120,5,70202,14,-1,-14
7139,5,70316,17,-1,-17
7169,5,70561,11,-1,-11
7177,5,70626,12,-1,-12
7195,5,70744,12,-1,-12
7201,5,70783,7,-1,-7
7229,5,70897,13,-1,-13
7240,5,70977,16,-1,-16
7242,5,70995,6,-1,-6
7252,5,71060,9,-1,-9
7270,5,71120,10,-1,-10
7280,5,71215,14,-1,-14
7292,5,71267,15,-1,-15
7303,5,71346,13,-1,-13
7314,5,71440,9,-1,-9
7318,5,71456,15,-1,-15
7359,5,71738,6,-1,-6
7406,5,72060,13,-1,-13
7429,5,72219,7,-1,-7
7452,5,72322,15,-1,-15
7457,5,72365,8,-1,-8
7473,5,72473,22,-1,-22
7475,5,72485,12,-1,-12
7492,5,72578,6,-1,-6
7569,5,73026,6,-1,-6
7602,5,73221,15,-1,-15
7657,5,73544,15,-1,-15
7675,5,73653,9,-1,-9
7719,5,73903,13,-1,-13
7843,5,74495,7,-1,-7
7900,5,74818,10,-1,-10
7930,5,74995,12,-1,-12
8109,5,75925,13,-1,-13
8304,5,76961,13,-1,-13
8331,5,77090,17,-1,-17
8376,5,77291,11,-1,-11
8414,5,77444,12,-1,-12
8626,5,78506,8,-1,-8
8637,5,78574,14,-1,-14
9134,5,80691,10,-1,-10
10206,5,85419,12,-1,-12
11736,5,91986,12,-1,-12
12743,5,97175,13,-1,-13
12956,5,98515,13,-1,-13
13400,5,101566,15,-1,-15
13479,5,102237,13,-1,-13
13760,5,104598,12,-1,-12
13827,5,105190,9,-1,-9
13831,5,105260,13,-1,-13
13913,5,106136,15,-1,-15
14498,5,137444,288,-1,-288
14516,5,140350,9,-1,-9
32040,21,5,38,-1,-38
35459,21,59937,1,-1,-1
35473,21,60155,1,-1,-1
35586,21,61164,1,-1,-1
35776,21,62734,1,-1,-1
36180,21,66127,1,-1,-1
36587,21,70626,1,-1,-1
36616,21,70977,1,-1,-1
36649,21,71440,1,-1,-1
36732,21,72473,1,-1,-1
36734,21,72485,2,-1,-2
36806,21,73544,1,-1,-1
36875,21,74495,2,-1,-2
38999,21,140350,1,-1,-1
757825,494,5,144,1,144
758405,494,137444,51,1,51
1000645,775,5,918,1,918
1000681,775,494,1,1,1
1001593,775,137444,344,1,344
1598110,1572,5,83,-1,-83
1598130,1572,494,2,-1,-2
12295949,52515,5,41,1,41
12295998,52515,137444,2,1,2
12955015,57252,5,42,1,42
13313258,59937,5,38,1,38
13341831,60155,5,39,1,39
13341832,60155,21,1,1,1
13381518,60450,5,41,1,41
13419754,60737,5,38,1,38
13475274,61164,5,41,1,41
13504915,61387,5,39,1,39
13547075,61721,5,43,1,43
13557213,61804,5,50,1,50
13574773,61941,5,40,1,40
13615444,62257,5,55,1,55
13674106,62700,5,46,1,46
13674164,62700,137444,3,1,3
13678358,62734,5,42,1,42
13685724,62795,5,51,1,51
13696886,62888,5,38,1,38
13715954,63041,5,47,1,47
13724287,63106,5,38,1,38
13732392,63173,5,41,1,41
13747092,63287,5,42,1,42
13752625,63339,5,42,1,42
13752679,63339,137444,1,1,1
13826190,63918,5,41,1,41
13862959,64210,5,45,1,45
13865226,64233,5,44,1,44
13892964,64443,5,40,1,40
13902952,64514,5,42,1,42
13965351,65001,5,38,1,38
13995138,65235,5,38,1,38
13998219,65262,5,47,1,47
14012171,65374,5,40,1,40
14036002,65565,5,40,1,40
14051671,65691,5,40,1,40
14051724,65691,137444,2,1,2
14057223,65742,5,39,1,39
14089055,66003,5,39,1,39
14103762,66127,5,40,1,40
14103763,66127,21,1,1,1
14103828,66127,137444,1,1,1
14115991,66225,5,49,1,49
14116044,66225,137444,1,1,1
14117243,66237,5,46,1,46
14154260,66541,5,41,1,41
14160491,66598,5,41,1,41
14190586,66843,5,38,1,38
14203317,66958,5,38,1,38
14216589,67072,5,45,1,45
14223566,67135,5,39,1,39
14235496,67241,5,49,1,49
14241113,67284,5,43,1,43
14245371,67324,5,46,1,46
14251187,67373,5,46,1,46
14259668,67453,5,45,1,45
14259716,67453,137444,3,1,3
14263074,67483,5,40,1,40
14269476,67537,5,42,1,42
14275638,67594,5,41,1,41
14328312,68056,5,40,1,40
14330661,68074,5,44,1,44
14344037,68192,5,39,1,39
14344600,68199,5,46,1,46
14362012,68355,5,40,1,40
14362062,68355,137444,1,1,1
14383249,68543,5,40,1,40
14392791,68617,5,45,1,45
14396678,68656,5,43,1,43
14418963,68838,5,40,1,40
14420756,68856,5,50,1,50
14422393,68869,5,40,1,40
14422447,68869,137444,1,1,1
14450763,69139,5,42,1,42
14481744,69415,5,39,1,39
14492363,69516,5,41,1,41
14495130,69540,5,40,1,40
14525565,69809,5,43,1,43
14531569,69860,5,41,1,41
14531613,69860,137444,4,1,4
14543009,69966,5,40,1,40
14544704,69982,5,39,1,39
14550492,70036,5,39,1,39
14568648,70202,5,40,1,40
14581777,70316,5,44,1,44
14610749,70561,5,39,1,39
14618088,70626,5,52,1,52
14631240,70744,5,39,1,39
14635726,70783,5,39,1,39
14647714,70897,5,41,1,41
14656327,70977,5,39,1,39
14658186,70995,5,46,1,46
14664944,71060,5,41,1,41
14664945,71060,494,1,1,1
14670187,71120,5,40,1,40
14680828,71215,5,43,1,43
14685821,71267,5,42,1,42
14693690,71346,5,39,1,39
14703808,71440,5,41,1,41
14705510,71456,5,44,1,44
14705563,71456,137444,1,1,1
14736062,71738,5,40,1,40
14736115,71738,137444,1,1,1
14771140,72060,5,41,1,41
14788752,72219,5,39,1,39
14788800,72219,137444,2,1,2
14799628,72322,5,45,1,45
14804288,72365,5,42,1,42
14815151,72473,5,41,1,41
14815152,72473,21,1,1,1
14816101,72485,5,43,1,43
14825618,72578,5,39,1,39
14825674,72578,137444,1,1,1
14872749,73026,5,41,1,41
14892579,73221,5,39,1,39
14926968,73544,5,45,1,45
14937066,73653,5,39,1,39
14937113,73653,137444,8,1,8
14962271,73903,5,39,1,39
15024378,74495,5,41,1,41
15059020,74818,5,39,1,39
15076955,74995,5,42,1,42
15077006,74995,137444,1,1,1
15171001,75925,5,41,1,41
15275022,76961,5,39,1,39
15275068,76961,137444,1,1,1
15288253,77090,5,40,1,40
15307290,77291,5,40,1,40
15322987,77444,5,40,1,40
15430092,78506,5,44,1,44
15437423,78574,5,39,1,39
15646184,80691,5,39,1,39
16096773,85419,5,41,1,41
16690249,91986,5,44,1,44
17152556,97175,5,43,1,43
17272361,98515,5,39,1,39
17544131,101566,5,40,1,40
17604485,102237,5,40,1,40
17808131,104598,5,41,1,41
17856109,105190,5,39,1,39
17856154,105190,137444,7,1,7
17861992,105260,5,43,1,43
17937702,106136,5,41,1,41
21606907,137444,5,126,-1,-126
21606909,137444,21,3,-1,-3
21606916,137444,494,1,-1,-1
21606921,137444,775,6,-1,-6
21607306,137444,59937,1,-1,-1
21607319,137444,60737,1,-1,-1
21607331,137444,61721,1,-1,-1
21607338,137444,61941,1,-1,-1
21607352,137444,62734,1,-1,-1
21607353,137444,62795,1,-1,-1
21607365,137444,63173,1,-1,-1
21607366,137444,63287,1,-1,-1
21607400,137444,64443,1,-1,-1
21607432,137444,65742,1,-1,-1
21607440,137444,66003,1,-1,-1
21607475,137444,67373,2,-1,-2
21607506,137444,68199,2,-1,-2
21607518,137444,68617,1,-1,-1
21607582,137444,69966,1,-1,-1
21607588,137444,70036,1,-1,-1
21607623,137444,70626,1,-1,-1
21607633,137444,70897,3,-1,-3
21607657,137444,71346,1,-1,-1
21607714,137444,72578,1,-1,-1
21607833,137444,74495,1,-1,-1
21607852,137444,74818,2,-1,-2
21609321,137444,91986,1,-1,-1
21610281,137444,101566,1,-1,-1
21610534,137444,105260,1,-1,-1
21934630,140350,5,45,1,45
1 edge_id source_neuron_idx target_neuron_idx raw_count source_sign signed_raw
2 4897 5 21 2 -1 -2
3 4914 5 775 12 -1 -12
4 4919 5 1572 3 -1 -3
5 6148 5 52515 15 -1 -15
6 6240 5 57252 9 -1 -9
7 6305 5 59937 10 -1 -10
8 6310 5 60155 17 -1 -17
9 6320 5 60450 9 -1 -9
10 6329 5 60737 11 -1 -11
11 6351 5 61164 12 -1 -12
12 6354 5 61387 9 -1 -9
13 6368 5 61721 13 -1 -13
14 6371 5 61804 11 -1 -11
15 6378 5 61941 9 -1 -9
16 6396 5 62257 12 -1 -12
17 6412 5 62700 12 -1 -12
18 6415 5 62734 8 -1 -8
19 6421 5 62795 9 -1 -9
20 6428 5 62888 12 -1 -12
21 6435 5 63041 12 -1 -12
22 6437 5 63106 9 -1 -9
23 6440 5 63173 14 -1 -14
24 6448 5 63287 7 -1 -7
25 6452 5 63339 10 -1 -10
26 6491 5 63918 13 -1 -13
27 6505 5 64210 13 -1 -13
28 6508 5 64233 9 -1 -9
29 6521 5 64443 11 -1 -11
30 6529 5 64514 14 -1 -14
31 6550 5 65001 14 -1 -14
32 6568 5 65235 5 -1 -5
33 6569 5 65262 12 -1 -12
34 6574 5 65374 13 -1 -13
35 6584 5 65565 17 -1 -17
36 6598 5 65691 10 -1 -10
37 6602 5 65742 7 -1 -7
38 6617 5 66003 12 -1 -12
39 6626 5 66127 15 -1 -15
40 6636 5 66225 10 -1 -10
41 6637 5 66237 9 -1 -9
42 6660 5 66541 8 -1 -8
43 6666 5 66598 14 -1 -14
44 6693 5 66843 11 -1 -11
45 6705 5 66958 15 -1 -15
46 6714 5 67072 14 -1 -14
47 6722 5 67135 13 -1 -13
48 6738 5 67241 15 -1 -15
49 6743 5 67284 13 -1 -13
50 6749 5 67324 6 -1 -6
51 6754 5 67373 16 -1 -16
52 6765 5 67453 15 -1 -15
53 6770 5 67483 16 -1 -16
54 6776 5 67537 12 -1 -12
55 6783 5 67594 11 -1 -11
56 6832 5 68056 10 -1 -10
57 6836 5 68074 14 -1 -14
58 6854 5 68192 11 -1 -11
59 6856 5 68199 11 -1 -11
60 6872 5 68355 9 -1 -9
61 6894 5 68543 16 -1 -16
62 6904 5 68617 10 -1 -10
63 6910 5 68656 16 -1 -16
64 6934 5 68838 9 -1 -9
65 6937 5 68856 11 -1 -11
66 6938 5 68869 7 -1 -7
67 6970 5 69139 16 -1 -16
68 7007 5 69415 5 -1 -5
69 7019 5 69516 6 -1 -6
70 7022 5 69540 11 -1 -11
71 7064 5 69809 14 -1 -14
72 7073 5 69860 10 -1 -10
73 7084 5 69966 9 -1 -9
74 7087 5 69982 7 -1 -7
75 7092 5 70036 13 -1 -13
76 7120 5 70202 14 -1 -14
77 7139 5 70316 17 -1 -17
78 7169 5 70561 11 -1 -11
79 7177 5 70626 12 -1 -12
80 7195 5 70744 12 -1 -12
81 7201 5 70783 7 -1 -7
82 7229 5 70897 13 -1 -13
83 7240 5 70977 16 -1 -16
84 7242 5 70995 6 -1 -6
85 7252 5 71060 9 -1 -9
86 7270 5 71120 10 -1 -10
87 7280 5 71215 14 -1 -14
88 7292 5 71267 15 -1 -15
89 7303 5 71346 13 -1 -13
90 7314 5 71440 9 -1 -9
91 7318 5 71456 15 -1 -15
92 7359 5 71738 6 -1 -6
93 7406 5 72060 13 -1 -13
94 7429 5 72219 7 -1 -7
95 7452 5 72322 15 -1 -15
96 7457 5 72365 8 -1 -8
97 7473 5 72473 22 -1 -22
98 7475 5 72485 12 -1 -12
99 7492 5 72578 6 -1 -6
100 7569 5 73026 6 -1 -6
101 7602 5 73221 15 -1 -15
102 7657 5 73544 15 -1 -15
103 7675 5 73653 9 -1 -9
104 7719 5 73903 13 -1 -13
105 7843 5 74495 7 -1 -7
106 7900 5 74818 10 -1 -10
107 7930 5 74995 12 -1 -12
108 8109 5 75925 13 -1 -13
109 8304 5 76961 13 -1 -13
110 8331 5 77090 17 -1 -17
111 8376 5 77291 11 -1 -11
112 8414 5 77444 12 -1 -12
113 8626 5 78506 8 -1 -8
114 8637 5 78574 14 -1 -14
115 9134 5 80691 10 -1 -10
116 10206 5 85419 12 -1 -12
117 11736 5 91986 12 -1 -12
118 12743 5 97175 13 -1 -13
119 12956 5 98515 13 -1 -13
120 13400 5 101566 15 -1 -15
121 13479 5 102237 13 -1 -13
122 13760 5 104598 12 -1 -12
123 13827 5 105190 9 -1 -9
124 13831 5 105260 13 -1 -13
125 13913 5 106136 15 -1 -15
126 14498 5 137444 288 -1 -288
127 14516 5 140350 9 -1 -9
128 32040 21 5 38 -1 -38
129 35459 21 59937 1 -1 -1
130 35473 21 60155 1 -1 -1
131 35586 21 61164 1 -1 -1
132 35776 21 62734 1 -1 -1
133 36180 21 66127 1 -1 -1
134 36587 21 70626 1 -1 -1
135 36616 21 70977 1 -1 -1
136 36649 21 71440 1 -1 -1
137 36732 21 72473 1 -1 -1
138 36734 21 72485 2 -1 -2
139 36806 21 73544 1 -1 -1
140 36875 21 74495 2 -1 -2
141 38999 21 140350 1 -1 -1
142 757825 494 5 144 1 144
143 758405 494 137444 51 1 51
144 1000645 775 5 918 1 918
145 1000681 775 494 1 1 1
146 1001593 775 137444 344 1 344
147 1598110 1572 5 83 -1 -83
148 1598130 1572 494 2 -1 -2
149 12295949 52515 5 41 1 41
150 12295998 52515 137444 2 1 2
151 12955015 57252 5 42 1 42
152 13313258 59937 5 38 1 38
153 13341831 60155 5 39 1 39
154 13341832 60155 21 1 1 1
155 13381518 60450 5 41 1 41
156 13419754 60737 5 38 1 38
157 13475274 61164 5 41 1 41
158 13504915 61387 5 39 1 39
159 13547075 61721 5 43 1 43
160 13557213 61804 5 50 1 50
161 13574773 61941 5 40 1 40
162 13615444 62257 5 55 1 55
163 13674106 62700 5 46 1 46
164 13674164 62700 137444 3 1 3
165 13678358 62734 5 42 1 42
166 13685724 62795 5 51 1 51
167 13696886 62888 5 38 1 38
168 13715954 63041 5 47 1 47
169 13724287 63106 5 38 1 38
170 13732392 63173 5 41 1 41
171 13747092 63287 5 42 1 42
172 13752625 63339 5 42 1 42
173 13752679 63339 137444 1 1 1
174 13826190 63918 5 41 1 41
175 13862959 64210 5 45 1 45
176 13865226 64233 5 44 1 44
177 13892964 64443 5 40 1 40
178 13902952 64514 5 42 1 42
179 13965351 65001 5 38 1 38
180 13995138 65235 5 38 1 38
181 13998219 65262 5 47 1 47
182 14012171 65374 5 40 1 40
183 14036002 65565 5 40 1 40
184 14051671 65691 5 40 1 40
185 14051724 65691 137444 2 1 2
186 14057223 65742 5 39 1 39
187 14089055 66003 5 39 1 39
188 14103762 66127 5 40 1 40
189 14103763 66127 21 1 1 1
190 14103828 66127 137444 1 1 1
191 14115991 66225 5 49 1 49
192 14116044 66225 137444 1 1 1
193 14117243 66237 5 46 1 46
194 14154260 66541 5 41 1 41
195 14160491 66598 5 41 1 41
196 14190586 66843 5 38 1 38
197 14203317 66958 5 38 1 38
198 14216589 67072 5 45 1 45
199 14223566 67135 5 39 1 39
200 14235496 67241 5 49 1 49
201 14241113 67284 5 43 1 43
202 14245371 67324 5 46 1 46
203 14251187 67373 5 46 1 46
204 14259668 67453 5 45 1 45
205 14259716 67453 137444 3 1 3
206 14263074 67483 5 40 1 40
207 14269476 67537 5 42 1 42
208 14275638 67594 5 41 1 41
209 14328312 68056 5 40 1 40
210 14330661 68074 5 44 1 44
211 14344037 68192 5 39 1 39
212 14344600 68199 5 46 1 46
213 14362012 68355 5 40 1 40
214 14362062 68355 137444 1 1 1
215 14383249 68543 5 40 1 40
216 14392791 68617 5 45 1 45
217 14396678 68656 5 43 1 43
218 14418963 68838 5 40 1 40
219 14420756 68856 5 50 1 50
220 14422393 68869 5 40 1 40
221 14422447 68869 137444 1 1 1
222 14450763 69139 5 42 1 42
223 14481744 69415 5 39 1 39
224 14492363 69516 5 41 1 41
225 14495130 69540 5 40 1 40
226 14525565 69809 5 43 1 43
227 14531569 69860 5 41 1 41
228 14531613 69860 137444 4 1 4
229 14543009 69966 5 40 1 40
230 14544704 69982 5 39 1 39
231 14550492 70036 5 39 1 39
232 14568648 70202 5 40 1 40
233 14581777 70316 5 44 1 44
234 14610749 70561 5 39 1 39
235 14618088 70626 5 52 1 52
236 14631240 70744 5 39 1 39
237 14635726 70783 5 39 1 39
238 14647714 70897 5 41 1 41
239 14656327 70977 5 39 1 39
240 14658186 70995 5 46 1 46
241 14664944 71060 5 41 1 41
242 14664945 71060 494 1 1 1
243 14670187 71120 5 40 1 40
244 14680828 71215 5 43 1 43
245 14685821 71267 5 42 1 42
246 14693690 71346 5 39 1 39
247 14703808 71440 5 41 1 41
248 14705510 71456 5 44 1 44
249 14705563 71456 137444 1 1 1
250 14736062 71738 5 40 1 40
251 14736115 71738 137444 1 1 1
252 14771140 72060 5 41 1 41
253 14788752 72219 5 39 1 39
254 14788800 72219 137444 2 1 2
255 14799628 72322 5 45 1 45
256 14804288 72365 5 42 1 42
257 14815151 72473 5 41 1 41
258 14815152 72473 21 1 1 1
259 14816101 72485 5 43 1 43
260 14825618 72578 5 39 1 39
261 14825674 72578 137444 1 1 1
262 14872749 73026 5 41 1 41
263 14892579 73221 5 39 1 39
264 14926968 73544 5 45 1 45
265 14937066 73653 5 39 1 39
266 14937113 73653 137444 8 1 8
267 14962271 73903 5 39 1 39
268 15024378 74495 5 41 1 41
269 15059020 74818 5 39 1 39
270 15076955 74995 5 42 1 42
271 15077006 74995 137444 1 1 1
272 15171001 75925 5 41 1 41
273 15275022 76961 5 39 1 39
274 15275068 76961 137444 1 1 1
275 15288253 77090 5 40 1 40
276 15307290 77291 5 40 1 40
277 15322987 77444 5 40 1 40
278 15430092 78506 5 44 1 44
279 15437423 78574 5 39 1 39
280 15646184 80691 5 39 1 39
281 16096773 85419 5 41 1 41
282 16690249 91986 5 44 1 44
283 17152556 97175 5 43 1 43
284 17272361 98515 5 39 1 39
285 17544131 101566 5 40 1 40
286 17604485 102237 5 40 1 40
287 17808131 104598 5 41 1 41
288 17856109 105190 5 39 1 39
289 17856154 105190 137444 7 1 7
290 17861992 105260 5 43 1 43
291 17937702 106136 5 41 1 41
292 21606907 137444 5 126 -1 -126
293 21606909 137444 21 3 -1 -3
294 21606916 137444 494 1 -1 -1
295 21606921 137444 775 6 -1 -6
296 21607306 137444 59937 1 -1 -1
297 21607319 137444 60737 1 -1 -1
298 21607331 137444 61721 1 -1 -1
299 21607338 137444 61941 1 -1 -1
300 21607352 137444 62734 1 -1 -1
301 21607353 137444 62795 1 -1 -1
302 21607365 137444 63173 1 -1 -1
303 21607366 137444 63287 1 -1 -1
304 21607400 137444 64443 1 -1 -1
305 21607432 137444 65742 1 -1 -1
306 21607440 137444 66003 1 -1 -1
307 21607475 137444 67373 2 -1 -2
308 21607506 137444 68199 2 -1 -2
309 21607518 137444 68617 1 -1 -1
310 21607582 137444 69966 1 -1 -1
311 21607588 137444 70036 1 -1 -1
312 21607623 137444 70626 1 -1 -1
313 21607633 137444 70897 3 -1 -3
314 21607657 137444 71346 1 -1 -1
315 21607714 137444 72578 1 -1 -1
316 21607833 137444 74495 1 -1 -1
317 21607852 137444 74818 2 -1 -2
318 21609321 137444 91986 1 -1 -1
319 21610281 137444 101566 1 -1 -1
320 21610534 137444 105260 1 -1 -1
321 21934630 140350 5 45 1 45
@@ -0,0 +1,129 @@
neuron_idx,body_id,annotation_type,neurotransmitter
5,10009,CT1,gaba
21,10029,LoVC16,glutamate
494,10532,MeVC26,acetylcholine
775,10836,OLVC3,acetylcholine
1572,11684,MeVPLp2,glutamate
52515,70245,Tm9,acetylcholine
57252,76118,Tm9,acetylcholine
59937,79501,Tm9,acetylcholine
60155,79781,Tm9,acetylcholine
60450,80142,Tm9,acetylcholine
60737,80501,Tm9,acetylcholine
61164,81041,Tm9,acetylcholine
61387,81301,Tm9,acetylcholine
61721,81734,Tm9,acetylcholine
61804,81831,Tm9,acetylcholine
61941,82003,Tm9,acetylcholine
62257,82394,Tm9,acetylcholine
62700,82937,Tm9,acetylcholine
62734,82979,Tm9,acetylcholine
62795,83060,Tm9,acetylcholine
62888,83185,Tm9,acetylcholine
63041,83375,Tm9,acetylcholine
63106,83462,Tm9,acetylcholine
63173,83545,Tm9,acetylcholine
63287,83686,Tm9,acetylcholine
63339,83753,Tm9,acetylcholine
63918,84478,Tm9,acetylcholine
64210,84851,Tm9,acetylcholine
64233,84878,Tm9,acetylcholine
64443,85140,Tm9,acetylcholine
64514,85236,Tm9,acetylcholine
65001,85889,Tm9,acetylcholine
65235,86195,Tm9,acetylcholine
65262,86232,Tm9,acetylcholine
65374,86371,Tm9,acetylcholine
65565,86617,Tm9,acetylcholine
65691,86786,Tm9,acetylcholine
65742,86856,Tm9,acetylcholine
66003,87208,Tm9,acetylcholine
66127,87381,Tm9,acetylcholine
66225,87504,Tm9,acetylcholine
66237,87520,Tm9,acetylcholine
66541,87925,Tm9,acetylcholine
66598,87994,Tm9,acetylcholine
66843,88320,Tm9,acetylcholine
66958,88475,Tm9,acetylcholine
67072,88621,Tm9,acetylcholine
67135,88713,Tm9,acetylcholine
67241,88850,Tm9,acetylcholine
67284,88907,Tm9,acetylcholine
67324,88959,Tm9,acetylcholine
67373,89023,Tm9,acetylcholine
67453,89138,Tm9,acetylcholine
67483,89177,Tm9,acetylcholine
67537,89251,Tm9,acetylcholine
67594,89319,Tm9,acetylcholine
68056,89933,Tm9,acetylcholine
68074,89954,Tm9,acetylcholine
68192,90120,Tm9,acetylcholine
68199,90133,Tm9,acetylcholine
68355,90340,Tm9,acetylcholine
68543,90590,Tm9,acetylcholine
68617,90688,Tm9,acetylcholine
68656,90733,Tm9,acetylcholine
68838,90991,Tm9,acetylcholine
68856,91014,Tm9,acetylcholine
68869,91034,Tm9,acetylcholine
69139,91392,Tm9,acetylcholine
69415,91766,Tm9,acetylcholine
69516,91900,Tm9,acetylcholine
69540,91933,Tm9,acetylcholine
69809,92279,Tm9,acetylcholine
69860,92356,Tm9,acetylcholine
69966,92497,Tm9,acetylcholine
69982,92521,Tm9,acetylcholine
70036,92592,Tm9,acetylcholine
70202,92805,Tm9,acetylcholine
70316,92954,Tm9,acetylcholine
70561,93271,Tm9,acetylcholine
70626,93347,Tm9,acetylcholine
70744,93499,Tm9,acetylcholine
70783,93556,Tm9,acetylcholine
70897,93720,Tm9,acetylcholine
70977,93824,Tm9,acetylcholine
70995,93845,Tm9,acetylcholine
71060,93923,Tm9,acetylcholine
71120,94000,Tm9,acetylcholine
71215,94133,Tm9,acetylcholine
71267,94199,Tm9,acetylcholine
71346,94296,Tm9,acetylcholine
71440,94423,Tm9,acetylcholine
71456,94443,Tm9,acetylcholine
71738,94831,Tm9,acetylcholine
72060,95260,Tm9,acetylcholine
72219,95470,Tm9,acetylcholine
72322,95604,Tm9,acetylcholine
72365,95658,Tm9,acetylcholine
72473,95798,Tm9,acetylcholine
72485,95813,Tm9,acetylcholine
72578,95937,Tm9,acetylcholine
73026,96565,Tm9,acetylcholine
73221,96845,Tm9,acetylcholine
73544,97254,Tm9,acetylcholine
73653,97408,Tm9,acetylcholine
73903,97731,Tm9,acetylcholine
74495,98585,Tm9,acetylcholine
74818,99046,Tm9,acetylcholine
74995,99268,Tm9,acetylcholine
75925,100512,Tm9,acetylcholine
76961,101957,Tm9,acetylcholine
77090,102126,Tm9,acetylcholine
77291,102421,Tm9,acetylcholine
77444,102619,Tm9,acetylcholine
78506,104080,Tm9,acetylcholine
78574,104167,Tm9,acetylcholine
80691,107094,Tm9,acetylcholine
85419,113692,Tm9,acetylcholine
91986,123393,Tm9,acetylcholine
97175,131756,Tm9,acetylcholine
98515,134011,Tm9,acetylcholine
101566,139750,Tm9,acetylcholine
102237,141068,Tm9,acetylcholine
104598,145922,Tm9,acetylcholine
105190,147208,Tm9,acetylcholine
105260,147382,Tm9,acetylcholine
106136,149457,Tm9,acetylcholine
137444,537032,LT33,gaba
140350,546479,Tm9,acetylcholine
1 neuron_idx body_id annotation_type neurotransmitter
2 5 10009 CT1 gaba
3 21 10029 LoVC16 glutamate
4 494 10532 MeVC26 acetylcholine
5 775 10836 OLVC3 acetylcholine
6 1572 11684 MeVPLp2 glutamate
7 52515 70245 Tm9 acetylcholine
8 57252 76118 Tm9 acetylcholine
9 59937 79501 Tm9 acetylcholine
10 60155 79781 Tm9 acetylcholine
11 60450 80142 Tm9 acetylcholine
12 60737 80501 Tm9 acetylcholine
13 61164 81041 Tm9 acetylcholine
14 61387 81301 Tm9 acetylcholine
15 61721 81734 Tm9 acetylcholine
16 61804 81831 Tm9 acetylcholine
17 61941 82003 Tm9 acetylcholine
18 62257 82394 Tm9 acetylcholine
19 62700 82937 Tm9 acetylcholine
20 62734 82979 Tm9 acetylcholine
21 62795 83060 Tm9 acetylcholine
22 62888 83185 Tm9 acetylcholine
23 63041 83375 Tm9 acetylcholine
24 63106 83462 Tm9 acetylcholine
25 63173 83545 Tm9 acetylcholine
26 63287 83686 Tm9 acetylcholine
27 63339 83753 Tm9 acetylcholine
28 63918 84478 Tm9 acetylcholine
29 64210 84851 Tm9 acetylcholine
30 64233 84878 Tm9 acetylcholine
31 64443 85140 Tm9 acetylcholine
32 64514 85236 Tm9 acetylcholine
33 65001 85889 Tm9 acetylcholine
34 65235 86195 Tm9 acetylcholine
35 65262 86232 Tm9 acetylcholine
36 65374 86371 Tm9 acetylcholine
37 65565 86617 Tm9 acetylcholine
38 65691 86786 Tm9 acetylcholine
39 65742 86856 Tm9 acetylcholine
40 66003 87208 Tm9 acetylcholine
41 66127 87381 Tm9 acetylcholine
42 66225 87504 Tm9 acetylcholine
43 66237 87520 Tm9 acetylcholine
44 66541 87925 Tm9 acetylcholine
45 66598 87994 Tm9 acetylcholine
46 66843 88320 Tm9 acetylcholine
47 66958 88475 Tm9 acetylcholine
48 67072 88621 Tm9 acetylcholine
49 67135 88713 Tm9 acetylcholine
50 67241 88850 Tm9 acetylcholine
51 67284 88907 Tm9 acetylcholine
52 67324 88959 Tm9 acetylcholine
53 67373 89023 Tm9 acetylcholine
54 67453 89138 Tm9 acetylcholine
55 67483 89177 Tm9 acetylcholine
56 67537 89251 Tm9 acetylcholine
57 67594 89319 Tm9 acetylcholine
58 68056 89933 Tm9 acetylcholine
59 68074 89954 Tm9 acetylcholine
60 68192 90120 Tm9 acetylcholine
61 68199 90133 Tm9 acetylcholine
62 68355 90340 Tm9 acetylcholine
63 68543 90590 Tm9 acetylcholine
64 68617 90688 Tm9 acetylcholine
65 68656 90733 Tm9 acetylcholine
66 68838 90991 Tm9 acetylcholine
67 68856 91014 Tm9 acetylcholine
68 68869 91034 Tm9 acetylcholine
69 69139 91392 Tm9 acetylcholine
70 69415 91766 Tm9 acetylcholine
71 69516 91900 Tm9 acetylcholine
72 69540 91933 Tm9 acetylcholine
73 69809 92279 Tm9 acetylcholine
74 69860 92356 Tm9 acetylcholine
75 69966 92497 Tm9 acetylcholine
76 69982 92521 Tm9 acetylcholine
77 70036 92592 Tm9 acetylcholine
78 70202 92805 Tm9 acetylcholine
79 70316 92954 Tm9 acetylcholine
80 70561 93271 Tm9 acetylcholine
81 70626 93347 Tm9 acetylcholine
82 70744 93499 Tm9 acetylcholine
83 70783 93556 Tm9 acetylcholine
84 70897 93720 Tm9 acetylcholine
85 70977 93824 Tm9 acetylcholine
86 70995 93845 Tm9 acetylcholine
87 71060 93923 Tm9 acetylcholine
88 71120 94000 Tm9 acetylcholine
89 71215 94133 Tm9 acetylcholine
90 71267 94199 Tm9 acetylcholine
91 71346 94296 Tm9 acetylcholine
92 71440 94423 Tm9 acetylcholine
93 71456 94443 Tm9 acetylcholine
94 71738 94831 Tm9 acetylcholine
95 72060 95260 Tm9 acetylcholine
96 72219 95470 Tm9 acetylcholine
97 72322 95604 Tm9 acetylcholine
98 72365 95658 Tm9 acetylcholine
99 72473 95798 Tm9 acetylcholine
100 72485 95813 Tm9 acetylcholine
101 72578 95937 Tm9 acetylcholine
102 73026 96565 Tm9 acetylcholine
103 73221 96845 Tm9 acetylcholine
104 73544 97254 Tm9 acetylcholine
105 73653 97408 Tm9 acetylcholine
106 73903 97731 Tm9 acetylcholine
107 74495 98585 Tm9 acetylcholine
108 74818 99046 Tm9 acetylcholine
109 74995 99268 Tm9 acetylcholine
110 75925 100512 Tm9 acetylcholine
111 76961 101957 Tm9 acetylcholine
112 77090 102126 Tm9 acetylcholine
113 77291 102421 Tm9 acetylcholine
114 77444 102619 Tm9 acetylcholine
115 78506 104080 Tm9 acetylcholine
116 78574 104167 Tm9 acetylcholine
117 80691 107094 Tm9 acetylcholine
118 85419 113692 Tm9 acetylcholine
119 91986 123393 Tm9 acetylcholine
120 97175 131756 Tm9 acetylcholine
121 98515 134011 Tm9 acetylcholine
122 101566 139750 Tm9 acetylcholine
123 102237 141068 Tm9 acetylcholine
124 104598 145922 Tm9 acetylcholine
125 105190 147208 Tm9 acetylcholine
126 105260 147382 Tm9 acetylcholine
127 106136 149457 Tm9 acetylcholine
128 137444 537032 LT33 gaba
129 140350 546479 Tm9 acetylcholine
@@ -0,0 +1,45 @@
{
"name": "highest_indegree",
"dt_ms": 0.1,
"steps": 500,
"duration_ms": 50.0,
"model": {
"dt_ms": 0.1,
"membrane_tau_ms": 20.0,
"synaptic_tau_ms": 5.0,
"refractory_ms": 2.2,
"resting_potential_mv": -52.0,
"reset_potential_mv": -52.0,
"threshold_mv": -45.0,
"synaptic_delay_ms": 1.8,
"w_syn_mv_per_anatomical_synapse": 0.275
},
"description": "Deterministically selected real induced graph; canonical edge IDs/counts and frozen signs.",
"trace_queue": true,
"validate_queue_proxy": true,
"selection": {
"target_neuron_idx": 5,
"target_body_id": 10009,
"global_in_degree": 11526,
"positive_in_edges": 10076,
"negative_in_edges": 1403,
"stimulated_neuron_idx": [
494,
775,
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62257,
62700,
62795,
63041,
65262,
66225,
66237,
67241,
67324,
68856,
70626,
137444
]
}
}
@@ -0,0 +1,267 @@
edge_id,source_neuron_idx,target_neuron_idx,raw_count,source_sign,signed_raw
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4899,5,44,4,-1,-4
4914,5,775,12,-1,-12
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6529,5,64514,14,-1,-14
6569,5,65262,12,-1,-12
6636,5,66225,10,-1,-10
6637,5,66237,9,-1,-9
6660,5,66541,8,-1,-8
6666,5,66598,14,-1,-14
6714,5,67072,14,-1,-14
6738,5,67241,15,-1,-15
6743,5,67284,13,-1,-13
6749,5,67324,6,-1,-6
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6765,5,67453,15,-1,-15
6776,5,67537,12,-1,-12
6783,5,67594,11,-1,-11
6836,5,68074,14,-1,-14
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7073,5,69860,10,-1,-10
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7242,5,70995,6,-1,-6
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7280,5,71215,14,-1,-14
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7314,5,71440,9,-1,-9
7318,5,71456,15,-1,-15
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7444,5,72275,1,-1,-1
7452,5,72322,15,-1,-15
7457,5,72365,8,-1,-8
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7475,5,72485,12,-1,-12
7569,5,73026,6,-1,-6
7657,5,73544,15,-1,-15
7930,5,74995,12,-1,-12
8626,5,78506,8,-1,-8
11736,5,91986,12,-1,-12
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32040,21,5,38,-1,-38
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13212942,59164,1572,1,-1,-1
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13475274,61164,5,41,1,41
13545470,61707,5,12,-1,-12
13547075,61721,5,43,1,43
13557213,61804,5,50,1,50
13615444,62257,5,55,1,55
13674106,62700,5,46,1,46
13674108,62700,6667,1,1,1
13674164,62700,137444,3,1,3
13678358,62734,5,42,1,42
13685724,62795,5,51,1,51
13715954,63041,5,47,1,47
13732392,63173,5,41,1,41
13747092,63287,5,42,1,42
13752625,63339,5,42,1,42
13752679,63339,137444,1,1,1
13826190,63918,5,41,1,41
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13865226,64233,5,44,1,44
13879186,64335,5,10,-1,-10
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14070553,65848,5,9,-1,-9
14115991,66225,5,49,1,49
14116044,66225,137444,1,1,1
14117243,66237,5,46,1,46
14154260,66541,5,41,1,41
14160491,66598,5,41,1,41
14216589,67072,5,45,1,45
14235496,67241,5,49,1,49
14241113,67284,5,43,1,43
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14245378,67324,49476,2,1,2
14251187,67373,5,46,1,46
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14330661,68074,5,44,1,44
14344600,68199,5,46,1,46
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14396678,68656,5,43,1,43
14412757,68785,5,9,-1,-9
14420756,68856,5,50,1,50
14450763,69139,5,42,1,42
14486521,69462,5,10,-1,-10
14492363,69516,5,41,1,41
14525565,69809,5,43,1,43
14531569,69860,5,41,1,41
14531613,69860,137444,4,1,4
14534259,69887,5,8,-1,-8
14581777,70316,5,44,1,44
14618088,70626,5,52,1,52
14622283,70662,5,8,-1,-8
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14658186,70995,5,46,1,46
14664944,71060,5,41,1,41
14664945,71060,494,1,1,1
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14685821,71267,5,42,1,42
14703808,71440,5,41,1,41
14705510,71456,5,44,1,44
14705563,71456,137444,1,1,1
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14815151,72473,5,41,1,41
14815152,72473,21,1,1,1
14816101,72485,5,43,1,43
14816111,72485,51102,2,1,2
14872749,73026,5,41,1,41
14926968,73544,5,45,1,45
15064838,74878,5,9,-1,-9
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15077006,74995,137444,1,1,1
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16448792,89271,1572,1,-1,-1
16690249,91986,5,44,1,44
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17152556,97175,5,43,1,43
17840952,104999,5,8,-1,-8
17861992,105260,5,43,1,43
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21606916,137444,494,1,-1,-1
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21607331,137444,61721,1,-1,-1
21607352,137444,62734,1,-1,-1
21607353,137444,62795,1,-1,-1
21607365,137444,63173,1,-1,-1
21607366,137444,63287,1,-1,-1
21607475,137444,67373,2,-1,-2
21607506,137444,68199,2,-1,-2
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21607623,137444,70626,1,-1,-1
21607633,137444,70897,3,-1,-3
21609321,137444,91986,1,-1,-1
21610534,137444,105260,1,-1,-1
21934630,140350,5,45,1,45
1 edge_id source_neuron_idx target_neuron_idx raw_count source_sign signed_raw
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100 758405 494 137444 51 1 51
101 1000645 775 5 918 1 918
102 1000652 775 44 2 1 2
103 1000681 775 494 1 1 1
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158 13345851 60182 5 8 -1 -8
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160 13475274 61164 5 41 1 41
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163 13557213 61804 5 50 1 50
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167 13674164 62700 137444 3 1 3
168 13678358 62734 5 42 1 42
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213 14618088 70626 5 52 1 52
214 14622283 70662 5 8 -1 -8
215 14647714 70897 5 41 1 41
216 14654473 70959 5 8 -1 -8
217 14658186 70995 5 46 1 46
218 14664944 71060 5 41 1 41
219 14664945 71060 494 1 1 1
220 14680828 71215 5 43 1 43
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240 16053751 84964 5 9 -1 -9
241 16298962 87611 5 9 -1 -9
242 16407272 88823 5 15 -1 -15
243 16448785 89271 5 10 -1 -10
244 16448792 89271 1572 1 -1 -1
245 16690249 91986 5 44 1 44
246 17112927 96731 5 12 -1 -12
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249 17840952 104999 5 8 -1 -8
250 17861992 105260 5 43 1 43
251 21606907 137444 5 126 -1 -126
252 21606909 137444 21 3 -1 -3
253 21606916 137444 494 1 -1 -1
254 21606921 137444 775 6 -1 -6
255 21607331 137444 61721 1 -1 -1
256 21607352 137444 62734 1 -1 -1
257 21607353 137444 62795 1 -1 -1
258 21607365 137444 63173 1 -1 -1
259 21607366 137444 63287 1 -1 -1
260 21607475 137444 67373 2 -1 -2
261 21607506 137444 68199 2 -1 -2
262 21607518 137444 68617 1 -1 -1
263 21607623 137444 70626 1 -1 -1
264 21607633 137444 70897 3 -1 -3
265 21609321 137444 91986 1 -1 -1
266 21610534 137444 105260 1 -1 -1
267 21934630 140350 5 45 1 45
@@ -0,0 +1,129 @@
neuron_idx,body_id,annotation_type,neurotransmitter
5,10009,CT1,gaba
21,10029,LoVC16,glutamate
44,10053,LoVC16,glutamate
494,10532,MeVC26,acetylcholine
775,10836,OLVC3,acetylcholine
1572,11684,MeVPLp2,glutamate
6667,17289,Li29,gaba
15460,27130,TmY15,gaba
18811,30896,TmY15,gaba
40937,56248,Mi9,glutamate
41813,57306,Mi9,glutamate
42005,57537,Mi9,glutamate
45077,61192,Mi9,glutamate
45449,61642,Mi9,glutamate
47004,63517,Mi9,glutamate
47028,63547,Mi9,glutamate
47491,64100,Mi9,glutamate
47754,64418,Mi9,glutamate
47782,64451,Mi9,glutamate
48155,64903,Mi9,glutamate
48221,64984,Mi9,glutamate
48947,65851,Mi9,glutamate
49476,66497,Mi9,glutamate
49706,66780,Mi9,glutamate
49809,66902,Mi9,glutamate
50350,67574,Mi9,glutamate
50455,67707,Mi9,glutamate
50561,67843,Mi9,glutamate
50622,67918,Mi9,glutamate
50734,68057,Mi9,glutamate
51102,68508,Mi9,glutamate
51157,68577,Mi9,glutamate
51608,69132,Mi9,glutamate
52222,69881,Mi9,glutamate
52352,70046,Mi9,glutamate
52494,70216,Mi9,glutamate
52515,70245,Tm9,acetylcholine
53063,70927,Mi9,glutamate
53324,71249,Mi9,glutamate
54010,72095,Mi9,glutamate
55100,73466,Mi9,glutamate
56539,75256,Mi9,glutamate
57201,76059,Mi9,glutamate
57252,76118,Tm9,acetylcholine
57612,76551,Mi9,glutamate
59164,78522,Mi9,glutamate
59365,78774,Mi9,glutamate
60182,79818,Mi9,glutamate
60450,80142,Tm9,acetylcholine
61164,81041,Tm9,acetylcholine
61707,81713,Mi9,glutamate
61721,81734,Tm9,acetylcholine
61804,81831,Tm9,acetylcholine
62257,82394,Tm9,acetylcholine
62700,82937,Tm9,acetylcholine
62734,82979,Tm9,acetylcholine
62795,83060,Tm9,acetylcholine
63041,83375,Tm9,acetylcholine
63173,83545,Tm9,acetylcholine
63287,83686,Tm9,acetylcholine
63339,83753,Tm9,acetylcholine
63918,84478,Tm9,acetylcholine
64210,84851,Tm9,acetylcholine
64233,84878,Tm9,acetylcholine
64335,85004,Mi9,glutamate
64514,85236,Tm9,acetylcholine
65262,86232,Tm9,acetylcholine
65848,86983,Mi9,glutamate
66225,87504,Tm9,acetylcholine
66237,87520,Tm9,acetylcholine
66541,87925,Tm9,acetylcholine
66598,87994,Tm9,acetylcholine
67072,88621,Tm9,acetylcholine
67241,88850,Tm9,acetylcholine
67284,88907,Tm9,acetylcholine
67324,88959,Tm9,acetylcholine
67373,89023,Tm9,acetylcholine
67453,89138,Tm9,acetylcholine
67537,89251,Tm9,acetylcholine
67594,89319,Tm9,acetylcholine
68074,89954,Tm9,acetylcholine
68199,90133,Tm9,acetylcholine
68506,90541,Mi9,glutamate
68617,90688,Tm9,acetylcholine
68656,90733,Tm9,acetylcholine
68785,90917,Mi9,glutamate
68856,91014,Tm9,acetylcholine
69139,91392,Tm9,acetylcholine
69462,91829,Mi9,glutamate
69516,91900,Tm9,acetylcholine
69809,92279,Tm9,acetylcholine
69860,92356,Tm9,acetylcholine
69887,92394,Mi9,glutamate
70316,92954,Tm9,acetylcholine
70626,93347,Tm9,acetylcholine
70662,93393,Mi9,glutamate
70897,93720,Tm9,acetylcholine
70959,93800,Mi9,glutamate
70995,93845,Tm9,acetylcholine
71060,93923,Tm9,acetylcholine
71215,94133,Tm9,acetylcholine
71267,94199,Tm9,acetylcholine
71440,94423,Tm9,acetylcholine
71456,94443,Tm9,acetylcholine
72060,95260,Tm9,acetylcholine
72275,95544,Mi9,glutamate
72322,95604,Tm9,acetylcholine
72365,95658,Tm9,acetylcholine
72473,95798,Tm9,acetylcholine
72485,95813,Tm9,acetylcholine
73026,96565,Tm9,acetylcholine
73544,97254,Tm9,acetylcholine
74878,99133,Mi9,glutamate
74995,99268,Tm9,acetylcholine
77390,102542,Mi9,glutamate
78506,104080,Tm9,acetylcholine
84964,113050,Mi9,glutamate
87611,116890,Mi9,glutamate
88823,118635,Mi9,glutamate
89271,119289,Mi9,glutamate
91986,123393,Tm9,acetylcholine
96731,131072,Mi9,glutamate
96909,131351,Mi9,glutamate
97175,131756,Tm9,acetylcholine
104999,146808,Mi9,glutamate
105260,147382,Tm9,acetylcholine
137444,537032,LT33,gaba
140350,546479,Tm9,acetylcholine
1 neuron_idx body_id annotation_type neurotransmitter
2 5 10009 CT1 gaba
3 21 10029 LoVC16 glutamate
4 44 10053 LoVC16 glutamate
5 494 10532 MeVC26 acetylcholine
6 775 10836 OLVC3 acetylcholine
7 1572 11684 MeVPLp2 glutamate
8 6667 17289 Li29 gaba
9 15460 27130 TmY15 gaba
10 18811 30896 TmY15 gaba
11 40937 56248 Mi9 glutamate
12 41813 57306 Mi9 glutamate
13 42005 57537 Mi9 glutamate
14 45077 61192 Mi9 glutamate
15 45449 61642 Mi9 glutamate
16 47004 63517 Mi9 glutamate
17 47028 63547 Mi9 glutamate
18 47491 64100 Mi9 glutamate
19 47754 64418 Mi9 glutamate
20 47782 64451 Mi9 glutamate
21 48155 64903 Mi9 glutamate
22 48221 64984 Mi9 glutamate
23 48947 65851 Mi9 glutamate
24 49476 66497 Mi9 glutamate
25 49706 66780 Mi9 glutamate
26 49809 66902 Mi9 glutamate
27 50350 67574 Mi9 glutamate
28 50455 67707 Mi9 glutamate
29 50561 67843 Mi9 glutamate
30 50622 67918 Mi9 glutamate
31 50734 68057 Mi9 glutamate
32 51102 68508 Mi9 glutamate
33 51157 68577 Mi9 glutamate
34 51608 69132 Mi9 glutamate
35 52222 69881 Mi9 glutamate
36 52352 70046 Mi9 glutamate
37 52494 70216 Mi9 glutamate
38 52515 70245 Tm9 acetylcholine
39 53063 70927 Mi9 glutamate
40 53324 71249 Mi9 glutamate
41 54010 72095 Mi9 glutamate
42 55100 73466 Mi9 glutamate
43 56539 75256 Mi9 glutamate
44 57201 76059 Mi9 glutamate
45 57252 76118 Tm9 acetylcholine
46 57612 76551 Mi9 glutamate
47 59164 78522 Mi9 glutamate
48 59365 78774 Mi9 glutamate
49 60182 79818 Mi9 glutamate
50 60450 80142 Tm9 acetylcholine
51 61164 81041 Tm9 acetylcholine
52 61707 81713 Mi9 glutamate
53 61721 81734 Tm9 acetylcholine
54 61804 81831 Tm9 acetylcholine
55 62257 82394 Tm9 acetylcholine
56 62700 82937 Tm9 acetylcholine
57 62734 82979 Tm9 acetylcholine
58 62795 83060 Tm9 acetylcholine
59 63041 83375 Tm9 acetylcholine
60 63173 83545 Tm9 acetylcholine
61 63287 83686 Tm9 acetylcholine
62 63339 83753 Tm9 acetylcholine
63 63918 84478 Tm9 acetylcholine
64 64210 84851 Tm9 acetylcholine
65 64233 84878 Tm9 acetylcholine
66 64335 85004 Mi9 glutamate
67 64514 85236 Tm9 acetylcholine
68 65262 86232 Tm9 acetylcholine
69 65848 86983 Mi9 glutamate
70 66225 87504 Tm9 acetylcholine
71 66237 87520 Tm9 acetylcholine
72 66541 87925 Tm9 acetylcholine
73 66598 87994 Tm9 acetylcholine
74 67072 88621 Tm9 acetylcholine
75 67241 88850 Tm9 acetylcholine
76 67284 88907 Tm9 acetylcholine
77 67324 88959 Tm9 acetylcholine
78 67373 89023 Tm9 acetylcholine
79 67453 89138 Tm9 acetylcholine
80 67537 89251 Tm9 acetylcholine
81 67594 89319 Tm9 acetylcholine
82 68074 89954 Tm9 acetylcholine
83 68199 90133 Tm9 acetylcholine
84 68506 90541 Mi9 glutamate
85 68617 90688 Tm9 acetylcholine
86 68656 90733 Tm9 acetylcholine
87 68785 90917 Mi9 glutamate
88 68856 91014 Tm9 acetylcholine
89 69139 91392 Tm9 acetylcholine
90 69462 91829 Mi9 glutamate
91 69516 91900 Tm9 acetylcholine
92 69809 92279 Tm9 acetylcholine
93 69860 92356 Tm9 acetylcholine
94 69887 92394 Mi9 glutamate
95 70316 92954 Tm9 acetylcholine
96 70626 93347 Tm9 acetylcholine
97 70662 93393 Mi9 glutamate
98 70897 93720 Tm9 acetylcholine
99 70959 93800 Mi9 glutamate
100 70995 93845 Tm9 acetylcholine
101 71060 93923 Tm9 acetylcholine
102 71215 94133 Tm9 acetylcholine
103 71267 94199 Tm9 acetylcholine
104 71440 94423 Tm9 acetylcholine
105 71456 94443 Tm9 acetylcholine
106 72060 95260 Tm9 acetylcholine
107 72275 95544 Mi9 glutamate
108 72322 95604 Tm9 acetylcholine
109 72365 95658 Tm9 acetylcholine
110 72473 95798 Tm9 acetylcholine
111 72485 95813 Tm9 acetylcholine
112 73026 96565 Tm9 acetylcholine
113 73544 97254 Tm9 acetylcholine
114 74878 99133 Mi9 glutamate
115 74995 99268 Tm9 acetylcholine
116 77390 102542 Mi9 glutamate
117 78506 104080 Tm9 acetylcholine
118 84964 113050 Mi9 glutamate
119 87611 116890 Mi9 glutamate
120 88823 118635 Mi9 glutamate
121 89271 119289 Mi9 glutamate
122 91986 123393 Tm9 acetylcholine
123 96731 131072 Mi9 glutamate
124 96909 131351 Mi9 glutamate
125 97175 131756 Tm9 acetylcholine
126 104999 146808 Mi9 glutamate
127 105260 147382 Tm9 acetylcholine
128 137444 537032 LT33 gaba
129 140350 546479 Tm9 acetylcholine
@@ -0,0 +1,45 @@
{
"name": "mixed_high_indegree",
"dt_ms": 0.1,
"steps": 500,
"duration_ms": 50.0,
"model": {
"dt_ms": 0.1,
"membrane_tau_ms": 20.0,
"synaptic_tau_ms": 5.0,
"refractory_ms": 2.2,
"resting_potential_mv": -52.0,
"reset_potential_mv": -52.0,
"threshold_mv": -45.0,
"synaptic_delay_ms": 1.8,
"w_syn_mv_per_anatomical_synapse": 0.275
},
"description": "Deterministically selected real induced graph; canonical edge IDs/counts and frozen signs.",
"trace_queue": true,
"validate_queue_proxy": true,
"selection": {
"target_neuron_idx": 5,
"target_body_id": 10009,
"global_in_degree": 11526,
"positive_in_edges": 10076,
"negative_in_edges": 1403,
"stimulated_neuron_idx": [
494,
775,
61804,
62257,
62700,
62795,
63041,
65262,
66225,
66237,
67241,
67324,
67373,
68199,
68856,
70626
]
}
}
@@ -0,0 +1,21 @@
{
"name": "motif_feedforward",
"dt_ms": 0.1,
"steps": 1000,
"duration_ms": 100.0,
"model": {
"dt_ms": 0.1,
"membrane_tau_ms": 20.0,
"synaptic_tau_ms": 5.0,
"refractory_ms": 2.2,
"resting_potential_mv": -52.0,
"reset_potential_mv": -52.0,
"threshold_mv": -45.0,
"synaptic_delay_ms": 1.8,
"w_syn_mv_per_anatomical_synapse": 0.275
},
"description": "Same selected4AC/4AD motif,100ms prefix; no new edge or weight.",
"trace_queue": true,
"validate_queue_proxy": true,
"selection": null
}
@@ -0,0 +1,21 @@
{
"name": "motif_five",
"dt_ms": 0.1,
"steps": 1000,
"duration_ms": 100.0,
"model": {
"dt_ms": 0.1,
"membrane_tau_ms": 20.0,
"synaptic_tau_ms": 5.0,
"refractory_ms": 2.2,
"resting_potential_mv": -52.0,
"reset_potential_mv": -52.0,
"threshold_mv": -45.0,
"synaptic_delay_ms": 1.8,
"w_syn_mv_per_anatomical_synapse": 0.275
},
"description": "Same selected4AC/4AD motif,100ms prefix; no new edge or weight.",
"trace_queue": true,
"validate_queue_proxy": true,
"selection": null
}
@@ -0,0 +1,21 @@
{
"name": "motif_no_input",
"dt_ms": 0.1,
"steps": 1000,
"duration_ms": 100.0,
"model": {
"dt_ms": 0.1,
"membrane_tau_ms": 20.0,
"synaptic_tau_ms": 5.0,
"refractory_ms": 2.2,
"resting_potential_mv": -52.0,
"reset_potential_mv": -52.0,
"threshold_mv": -45.0,
"synaptic_delay_ms": 1.8,
"w_syn_mv_per_anatomical_synapse": 0.275
},
"description": "Same selected4AC/4AD motif,100ms prefix; no new edge or weight.",
"trace_queue": true,
"validate_queue_proxy": true,
"selection": null
}
@@ -0,0 +1,21 @@
{
"name": "motif_pair",
"dt_ms": 0.1,
"steps": 1000,
"duration_ms": 100.0,
"model": {
"dt_ms": 0.1,
"membrane_tau_ms": 20.0,
"synaptic_tau_ms": 5.0,
"refractory_ms": 2.2,
"resting_potential_mv": -52.0,
"reset_potential_mv": -52.0,
"threshold_mv": -45.0,
"synaptic_delay_ms": 1.8,
"w_syn_mv_per_anatomical_synapse": 0.275
},
"description": "Same selected4AC/4AD motif,100ms prefix; no new edge or weight.",
"trace_queue": true,
"validate_queue_proxy": true,
"selection": null
}
@@ -0,0 +1,21 @@
{
"name": "motif_trio",
"dt_ms": 0.1,
"steps": 1000,
"duration_ms": 100.0,
"model": {
"dt_ms": 0.1,
"membrane_tau_ms": 20.0,
"synaptic_tau_ms": 5.0,
"refractory_ms": 2.2,
"resting_potential_mv": -52.0,
"reset_potential_mv": -52.0,
"threshold_mv": -45.0,
"synaptic_delay_ms": 1.8,
"w_syn_mv_per_anatomical_synapse": 0.275
},
"description": "Same selected4AC/4AD motif,100ms prefix; no new edge or weight.",
"trace_queue": true,
"validate_queue_proxy": true,
"selection": null
}
@@ -0,0 +1,21 @@
{
"name": "ordered_cancellation",
"dt_ms": 0.1,
"steps": 160,
"duration_ms": 16.0,
"model": {
"dt_ms": 0.1,
"membrane_tau_ms": 20.0,
"synaptic_tau_ms": 5.0,
"refractory_ms": 2.2,
"resting_potential_mv": -52.0,
"reset_potential_mv": -52.0,
"threshold_mv": -45.0,
"synaptic_delay_ms": 1.8,
"w_syn_mv_per_anatomical_synapse": 0.275
},
"description": "Pure synthetic numerical cancellation; huge counts are nonphysiological; nonzero initial g.",
"trace_queue": true,
"validate_queue_proxy": true,
"selection": null
}
@@ -0,0 +1,21 @@
{
"name": "release_selfloop",
"dt_ms": 0.1,
"steps": 160,
"duration_ms": 16.0,
"model": {
"dt_ms": 0.1,
"membrane_tau_ms": 20.0,
"synaptic_tau_ms": 5.0,
"refractory_ms": 2.2,
"resting_potential_mv": -52.0,
"reset_potential_mv": -52.0,
"threshold_mv": -45.0,
"synaptic_delay_ms": 1.8,
"w_syn_mv_per_anatomical_synapse": 0.275
},
"description": "Release at22 with same-tick force/arrival, loops and ring wraps.",
"trace_queue": true,
"validate_queue_proxy": true,
"selection": null
}
@@ -0,0 +1,21 @@
{
"name": "signed_random",
"dt_ms": 0.1,
"steps": 400,
"duration_ms": 40.0,
"model": {
"dt_ms": 0.1,
"membrane_tau_ms": 20.0,
"synaptic_tau_ms": 5.0,
"refractory_ms": 2.2,
"resting_potential_mv": -52.0,
"reset_potential_mv": -52.0,
"threshold_mv": -45.0,
"synaptic_delay_ms": 1.8,
"w_syn_mv_per_anatomical_synapse": 0.275
},
"description": "Fixed seed128-cell signed random fixture; forcing stops at20ms; signs assigned per synthetic source.",
"trace_queue": true,
"validate_queue_proxy": true,
"selection": null
}
@@ -0,0 +1,21 @@
{
"name": "threshold_rounding",
"dt_ms": 0.1,
"steps": 100,
"duration_ms": 10.0,
"model": {
"dt_ms": 0.1,
"membrane_tau_ms": 20.0,
"synaptic_tau_ms": 5.0,
"refractory_ms": 2.2,
"resting_potential_mv": -52.0,
"reset_potential_mv": -52.0,
"threshold_mv": -45.0,
"synaptic_delay_ms": 1.8,
"w_syn_mv_per_anatomical_synapse": 0.275
},
"description": "Preintegration voltages bracket strict-threshold landing in both precisions; inert zero fixture edge.",
"trace_queue": true,
"validate_queue_proxy": true,
"selection": null
}
@@ -0,0 +1,917 @@
b868f1af329c95f5c264e42b05f53b13b6a86ef4a8a4ffb728cb8eee1e6f4223 REPORT.md
ea2305390f21d0aa35de76efae59819cb5548391b6cab57b9d72a3472d017013 audit/final-status.json
f3a900e9032754619549005131283cbaf87fdaa9093887a90a295589dac590fb cases/fanin_bursts/arrays.npz
90850d6968a7832e415d0966b57e216a65f5ba8f63e488e497148b6525806d79 cases/fanin_bursts/spec.json
aec331ee87f0da905d55b4dbb61a7e72fae734246ae6fbf462d7f6348dcdae1b cases/highest_indegree/anatomical-edges.csv
02ab938969b4369b1e08bc4a82e8c5fe68683f7318baf5eb052098d63fab0302 cases/highest_indegree/arrays.npz
5a837548ac8d45d495765d012f4495f4f68b2c9b0eda3eabd759869fc793fe77 cases/highest_indegree/identities.csv
eab2c18f120fd3e9f067f489cee0a4c07477607facf3094083d3e3fa60bfd1ef cases/highest_indegree/spec.json
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